Unmet Needs: Adverse Childhood Experiences and Mental Health

UNMET NEEDS: ADVERSE CHILDHOOD EXPERIENCES AND MENTAL HEALTH

ISSUES AS PATHWAYS TO RECIDIVISM IN JUSTICE-INVOLVED YOUTH

MELISSA ALLYN KOWALSKI

A dissertation submitted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY Department of Criminal Justice and Criminology

MAY 2019

To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of MELISSA

ALLYN KOWALSKI find it satisfactory and recommend that it be accepted.

Zachary K. Hamilton, Ph.D., Chair

Mary K. Stohr, Ph.D.

Amelie Pedneault, Ph.D.

Michael T. Baglivio, Ph.D.

ii ACKNOWLEDGMENTS

Over the past four years I have received much encouragement from many individuals, and this dissertation would not have been realized without that support. I would like to express my deep gratitude to my dissertation committee for their assistance, guidance, and feedback. I thank my chair, Zachary Hamilton, for fostering my interest in statistics and research when I thought it was not the route for me and for providing me with the skills and resources I needed to become a capable and successful scholar. I thank Mary Stohr for her keen editing, attention to detail, and always providing a nurturing environment and looking out for my best interests. I thank Amelie

Pedneault for her insightful feedback, push to include theory, and time spent reviewing my dissertation. I thank Michael Baglivio for his expertise on my dissertation subject and asking questions that made my work better.

My gratefulness extends to Craig Hemmens, who has always provided support and valuable feedback to help me become a successful teacher and researcher. Advice given by Dale Willits has also assisted me in strengthening my research skills. I would also like to thank all of the other faculty members of the Department of Criminal Justice and Criminology at Washington

State University who have helped shape my scholarly work, aided me in becoming a stronger academic, and stoked my continued interest in the criminal justice system. My special thanks are also extended to the staff of the department, whose work behind the scenes keeps the department running. To all of the other graduate students, I thank you for the support with research, teaching, and the Graduate Student Association as well as making my experience in graduate school wonderful. I would also like to express my appreciation to the Oregon Youth Authority, which has helped me further develop my research skills. The work you do is inspiring, and you have furthered my interest in conducting research in the juvenile justice system to help those in need. I

iii would also like to express my gratitude to the Washington State Center for Court Research in the

Administration Office of the Courts for allowing me to examine the data used for this dissertation and providing insight on any questions I had pertaining to the data.

To my parents, Lori and Daniel Cerutti and Duane Ray, I am indebted to you for teaching me the importance of a strong work ethic in addition to continuously supporting me. For my brother,

Bryant Ray, I thank you for challenging me to better myself and insisting that I pursue my doctorate. Finally, I thank my husband Erik Kowalski and son Quinten Kowalski. Erik, you have been there for me through the best and worst of times, always providing unconditional support and weathering my stubbornness and stress-provoked moodiness with an astounding amount of patience. Thank you for your love and always pushing me to better myself. Quinten, despite your toddler-driven tantrums and many nights of sleep lost (which also allowed me to complete a lot of work), thank you for showing me the importance of taking a break and enjoying the moment.

You are a little spitfire, and I would not change a thing about you.

iv UNMET NEEDS: ADVERSE CHILDHOOD EXPERIENCES AND MENTAL HEALTH

ISSUES AS PATHWAYS TO RECIDIVISM IN JUSTICE-INVOLVED YOUTH

Abstract

by Melissa Allyn Kowalski, Ph.D. Washington State University May 2019

Chair: Zachary K. Hamilton

Standard practice within the correctional system dictates that certain needs, such as antisocial personality, cognitions, and peers, are to be prioritized when providing interventions. However, greater concern has arisen regarding the prevalence of non-criminogenic needs, including traumatic experiences and mental health problems, in the justice-involved youth population and whether these youths’ needs are being met while in the juvenile justice system. The juvenile justice literature indicates that Adverse Childhood Experiences (ACEs), which may act as a proxy for trauma, and mental health concerns are prevalent in the justice-involved youth population. While these topics have been examined independently, the current study utilized a large sample of male (n = 38,100) and female (n = 12,762) youths on community supervision to identify whether these youths’ needs are currently being addressed in Washington State.

Moreover, the effect of programming on these youths’ adverse experiences and specific mental health problems (internalizing, externalizing, or co-occurring symptoms) was examined to ascertain whether provided interventions ameliorated the effect of these needs on youths’ recidivism. Life course theory was also tested to assess whether early-onset youth differed from

v those with a late onset on their reoffending. In a statistically weighted sample of youth with need-service matches versus those with mismatches, results demonstrated that mismatches were not associated with increased recidivism. Additionally, mediated path analyses showed that only substance abuse treatment impacts the relationship between ACEs and reoffending for males.

Internalizing symptoms presented as a protective factor that was mediated by substance use treatment. Conversely, both externalizing and co-occurring symptoms presented as risk factors, but the effect of the latter was decreased by substance abuse programming for males. Lastly, early-onset of deviance resulted in higher recidivism odds. These findings have implication for practice, insofar that they add context regarding which youth histories (ACEs) and attributes

(mental health symptomatology) affect treatment efficacy, thus potentially reducing risk to the public and improving youths’ personal well-being. In short, the results suggest a move away from a hyper-focus on risk to consideration of both youths’ needs and their responsivity to programming.

vi TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...... iii

ABSTRACT ...... v

LIST OF TABLES ...... ix

LIST OF FIGURES ...... x

CHAPTERS

CHAPTER ONE: INTRODUCTION ...... 1

Purpose of the Study ...... 13

CHAPTER TWO: REVIEW OF THE LITERATURE ...... 15

Life Course Theory ...... 16

Risk-Need-Responsivity Model ...... 18

The Positive Achievement Change Tool ...... 34

Adverse Childhood Experiences ...... 36

Youth and Mental Health Issues ...... 48

Prevention and Programming ...... 62

Gender Responsivity ...... 74

Summary ...... 85

The Current Study ...... 85

CHAPTER THREE: RESEARCH METHODOLOGY ...... 87

Research Questions ...... 87

Data ...... 88

Analytic Plan ...... 93

vii CHAPTER FOUR: FINDINGS ...... 100

Effect of Need-Service Matching on Recidivism ...... 101

ACEs as Potential Needs and Responsivity Factors ...... 110

Effect of MHPs on the Relationship between Gender and Recidivism ...... 118

Mental Health Symptoms as Needs and Responsivity Factors ...... 121

Test of Life Course Theory ...... 131

Summary ...... 133

CHAPTER FIVE: DISCUSSION ...... 135

Study Findings ...... 136

Limitations and Avenues for Future Research ...... 149

Policy Recommendations...... 156

Conclusion ...... 165

REFERENCES ...... 168

APPENDICES

APPENDIX A: PACT – FULL ASSESSMENT ...... 209

APPENDIX B: SAMPLE DESCRIPTIVES FOR PACT RESPONSES ...... 220

APPENDIX C: WASHINGTON STATE PACT EBP ELIGIBILITY ...... 230

APPENDIX D: PACT ITEMS USED TO CREATE ACES MEASURES ...... 231

viii LIST OF TABLES

Page

Table 1: Outcomes Related to ACEs in Adolescence and Adulthood ...... 42

Table 2: Sample Descriptives ...... 89

Table 3: AUC Industry-Standard Effect Size Ranges ...... 95

Table 4: Program Eligibility and Start for Youth ...... 102

Table 5: Comparison of the WA Sample to 2017 IPUMS, Ages 6 to 20 ...... 103

Table 6: Population-Based Relative Rate Index for Need Program Groups ...... 103

Table 7: Predictors of Need-Program Mismatches and Matches, N = 47,236 ...... 105

Table 8: Need-Program (Mis)Match Balancing Statistics ...... 107

Table 9: Recidivism Outcomes by Study Group ...... 107

Table 10: Recidivism Outcomes by Study Group Program Eligibility ...... 109

Table 11: Prevalence of ACEs for All Youth ...... 111

Table 12: Prevalence of ACEs for MHP Subsets ...... 112

Table 13: ACEs and Youth Recidivism ...... 115

Table 14: Mediation Analyses – ACEs, AOD & Recidivism ...... 118

Table 15: Mediation Analyses – INT, AOD & Recidivism...... 123

Table 16: Mediation Analyses – EXT, AOD & Recidivism ...... 124

Table 17: Mediation Analyses – Co-Occurring, AOD & Recidivism ...... 126

Table 18: Mental Health Subsets and Youth Recidivism ...... 130

Table 19: Correlations between ACEs, MHPs & Age of Onset ...... 132

Table 20: Age of Onset and Youth Recidivism, N = 45,735 ...... 133

ix LIST OF FIGURES

Page

Figure 1: Visual Representation of Related Concepts ...... 86

Figure 2: Programming and ACEs Mediated Path Model ...... 97

Figure 3: Gender and MHPs Mediated Path Model...... 98

Figure 4: Programming and MHPs Mediated Path Model ...... 98

Figure 5: Effects of Gender and INT Symptoms on Recidivism ...... 119

Figure 6: Effects of Gender and EXT Symptoms on Recidivism ...... 120

Figure 7: Effects of Gender and Co-Occurring Symptoms on Recidivism ...... 121

CHAPTER ONE

INTRODUCTION

One of the original goals of the juvenile justice system was to address factors that contributed to youths’ delinquency through the provision of rehabilitation via individualized services (Bernard & Kurlychek, 2010; Clarke, 2005; Rothman, 2012). Justice-involved youth present with a variety of dynamic, or changeable, characteristics that require programming.

Otherwise known as criminogenic risks and needs, such concerns include: antisocial personality and cognition (e.g., anger, lack of empathy); association with deviant peers; poor interpersonal relationships within the family, in school or at work; pursuit of antisocial leisure activities; or substance abuse (Bonta & Andrews, 2017). These issues are common contributors to crime, but they can be addressed through treatment. However, other potential programming targets not identified as criminogenic include a history of trauma and/or mental health problems (MHPs).

These attributes are highly prevalent in youthful offenders (Baglivio et al., 2014; Skowyra &

Cocozza, 2007) and can have life-long consequences (Moffitt, 1993). Yet, due to a lack of resources, prioritization of safety and order over treatment (Rothman, 2012), and an emphasis on risk rather than need and responsivity (Hubbard, 2007), many offenders do not receive programming that could (1) address their needs and (2) reduce their responsivity.

Stated otherwise, the rehabilitative goal of the juvenile justice system is at odds with the goal of protecting the community; individualized treatment was substituted with a more punitive one-size-fits-all approach following the Progressive Era (Redding, Goldstein, & Heilbrun, 2005;

Rothman, 2012) that did not address youths’ specific concerns (Polaschek, 2011; Taxman &

Caudy, 2015). As the juvenile justice system focused more on retribution, treatment matching based on youths’ individual needs waned (Feld, 1997). A punitive focus on youth offenders has

not worked. According to a meta-analysis conducted by Smith, Goggin, and Gendreau (2002), incarceration and intermediate sanctions were not associated with decreased offending in youth offenders. The juvenile justice system is in flux once again, and the Risk-Need-Responsivity

(RNR) framework advocated for by Andrews and Bonta (2010) can help foster rehabilitation.

The RNR model has been empirically validated (Andrews & Dowden, 2005), particularly for the adult system, and interventions following RNR are effective in reducing reoffending for high risk offenders (Andrews & Dowden, 2006). However, the RNR framework is still in its infancy in the juvenile justice system (Singh et al., 2014). Under the RNR model, reoffending is reduced by classifying offender risk and need and then identifying specific factors related to treatment responsivity that can be used to match interventions to offenders’ needs (Bonta &

Andrews, 2007). The risk principle, specifically, postulates that high-risk offenders should be assigned the most intense intervention (Bonta & Andrews, 2017). The need principle dictates that offenders’ dynamic, criminogenic needs should be targeted by interventions to reduce recidivism. Finally, the responsivity principle refers to identifying offender characteristics to maximize risk supervision and needs intervention.

Responsivity can be general or specific (Bonta, 1995, 2002; Bonta & Andrews, 2007).

The former involves the inclusion of cognitive-behavioral techniques in rehabilitation and implementing programming that is effective for the offending population in question. The latter refers to matching services with offenders’ cognitive (e.g., intelligence), personality (e.g., mental health and impulsivity), and sociocultural (e.g., gender and age) characteristics to reduce offending. General and specific responsivity must be considered by matching the intensity and type of treatment to individuals based on their risk and needs classification to effectively address criminogenic needs in high-risk offenders (Crites & Taxman, 2013). One way in which risk and

needs are determined is through risk/needs assessments (RNAs). Such tools have become a standard practice in the juvenile justice system (Wachter, 2015), and use of RNAs with youth offenders reduces reoffending (Luong & Wormith, 2011; Olver, Stockdale, & Wormith, 2009).

RNR, Assessments and Treatment

The risk principle has received the most attention (Farrington, Ttofi, & Piquero, 2016) even though adhering to the need and responsivity principles has been associated with positive effects (Peterson-Badali, Skilling, & Haqanee, 2015; Vieira, Skilling, Peterson-Badali, 2009).

There are also negative consequences related to a mismatch between youth offenders’ needs and the services they receive. For instance, some practitioners overidentify youths’ needs (Luong &

Wormith, 2011), and when youth receive an intervention intended for a need they do not have

(e.g., substance abuse treatment when a youth has not abused or is not dependent on substances), youth spend more time involved in the juvenile justice system and have greater exposure to antisocial peers (Vincent, 2011). As a result, the affected youth may be more likely to recidivate.

Apart from risk, assessment tools can be used to examine needs (Gottfredson & Moriarty,

2006). With the addition of needs, such tools can aid practitioners in determining appropriate levels of supervision and programming (Andrews, Bonta, & Wormith, 2006; Taxman,

Shepardson, & Byrne, 2004). However, practitioners may use their discretion to override assessment results, which can affect whether youths’ needs are addressed.

Insufficient resources can also impact whether youth receive appropriate interventions

(Steinhart, 2006). For instance, Shook and Sarri (2007) found that judges and probation officers overrode RNAs for many of their youth served because there were not programming spots available or because they disagreed with the instrument’s recommendations. Peterson-Badali et al. (2015) also found that 40% of youth on probation did not have their needs met; for those who

did, only 1.4 identified needs were targeted for services (p. 313). The authors further revealed that addressing needs resulted in reduced recidivism. To better provide sufficient services,

Taxman and Caudy (2015) suggest that it is necessary to know for whom an intervention is needed and when it should be implemented. Yet, research is lacking regarding whether interventions that match youths’ risks and needs, based on RNAs, impact reoffending (Hannah-

Moﬀat & Maurutto, 2003; Luong & Wormith, 2011). Research demonstrates some reductions in recidivism when interventions match needs. For instance, reoffending has been shown to decrease by 38% in a sample of high-risk youth on probation when interventions matched youths’ needs, but an 82% increase in recidivism when such interventions were not provided

(Luong & Wormith, 2011, p. 1191). Additionally, interventions that match individual needs are more effective than interventions that globally target criminogenic needs (Baglivio, Wolff,

Howell, Jackowski, & Greenwald, 2018; Vieira et al., 2009). As such, addressing youths’ individual needs appears to be essential to reducing recidivism. Still, responsivity may be the key in rehabilitating justice-involved youth.

Offender responsivity has the strongest effect on successful outcomes (Bonta & Andrews,

2017), as individualized interventions that address offenders’ characteristics, risks, and needs are the most efficacious (Hubbard, 2007; Kazdin, 2008; Taxman & Marlowe, 2006). Programs may not be effective if the intervention is not directly relevant to the offender (Cann, 2006;

McMurran & McCulloch, 2007; Vieira et al., 2009). Yet, the responsivity principle is frequently sacrificed in the RNR framework (Hubbard, 2007; Kennedy, 2000; Nee, Ellis, Morris, & Wilson,

2012), and practitioners may be unaware of what interventions to implement, as limited research has assessed how individual characteristics impact treatment outcomes (Crites & Taxman, 2013).

Moreover, Luong and Wormith (2011) found that practitioners did not identify responsivity factors for 80% of youth on probation (p.1189), indicating responsivity is frequently not considered in treatment decision-making. Of importance to the present study, mental health issues may also impact youths’ responsivity to a treatment even when criminogenic needs, such as substance use or antisocial personality, are identified (Osher, D’Amora, Plotkin, Jarrett, &

Eggleston, 2012). Mental health issues can result in functional impairment; thus, people experiencing it are less responsive to interventions until their symptoms are addressed. For instance, someone who experiences paranoid delusions may not trust a treatment provider until the paranoia is under control. A less extreme example involves depression, where symptoms such as poor concentration or hopelessness can interfere with successful programming completion (Osher et al., 2012). Consequently, the RNR model may not work for youth with mental health issues, unless, as suggested by Vincent (2011), RNAs incorporate items on clinical diagnoses and MHPs. If included, these items can facilitate proper need-service matching. One such assessment, the Positive Achievement of Change Tool (PACT), has a domain dedicated to mental health (Barnoski, 2004a, 2004c). The PACT has been validated across female and male youth (Baglivio, 2009), as well as across different racial/ethnic categories (Baglivio &

Jackowski, 2013), but it has not yet been validated for youth with mental disorders.

Gender is also a concern in RNAs and need-service matching because females and males may differentially react to treatment. The RNR model is currently gender-neutral, as it assumes that risk factors and criminogenic needs are the same for females and males (Vitopoulos et al.,

2012). Yet, risk factors validated for male youth may not apply to female youth (Covington,

2007); moreover, females may have different needs, such as MHPs and trauma (e.g., history of abuse), that have a large role in their risk to reoffend (Bloom, Owen, Rosenbaum, & Deschenes,

2003; Daly, 1992; Gavazzi, Yarcheck, & Chesney-Lind, 2006; McCabe, Lansing, Garland, &

Hough, 2002; Odgers, Moretti, & Reppucci, 2005; Van Voorhis, Salisbury, Wright, & Bauman,

2008). As such, gender-specific treatments may be necessary because both their needs (e.g., trauma, MHPs) and behaviors (e.g., prostitution, running away, and drug use) present as a harm to themselves rather than to their communities (Hubbard & Matthews, 2008). Unfortunately, instead of accounting for these gendered pathways to offending, an increasing number of female offenders have instead been classified as high-risk, resulting in stricter sanctions that may exacerbate their needs (Holtfreter & Morash, 2003). These high-risk classifications are often the result of classifying women’s behaviors, such as prostitution and drug use, as risk factors that result in higher risk scores. Accordingly, these behaviors are considered criminogenic factors, and they may instead be indicative of an underlying need (e.g., trauma or mental health issues).

As an example, Vitopoulos et al. (2012) found that female youth had a higher likelihood of being recommended for treatment, despite having the same need scores as male youth.

According to the authors, this discrepant gender effect could be the result of gender expectations, wherein clinicians were more likely to refer female youth with certain behaviors, such as inattention, impulsivity, and aggression, for treatment because these behaviors are considered atypical for females. In turn, such behaviors are viewed as problematic and targeted by programming. Moreover, Vitopoulos et al. (2012) found that need-service matches reduced reoffending for male youth on probation but not female youth, which may indicate that the service was not matched to the needs of these girls, potentially signifying a lack of information about criminogenic needs for female offenders, as well as gender being a responsivity factor in interventions. Vitopoulos and colleagues (2012) remarked that the assessment they examined

(the Youth Level of Service/Case Management Inventory [YLS/CMI]) may have been more

successful for male youth because the tool does not incorporate, or statistically weight, enough gender-specific factors essential to female treatment success, such as a history of abuse (Hubbard

& Pratt, 2002) or substance use (Hubbard & Matthews, 2008). Van Voorhis et al. (2010) demonstrated that inclusion of gender-specific items in risk assessments can better predict reoffending for adult females, which helps guide service planning. Thus, both assessments and treatment may perform better if they include gender-specific aspects.

Yet, even if a youth’s criminogenic needs are established, he or she may not receive services for that need (Flores, Travis, & Latessa, 2004; Latessa, Cullen, & Gendreau, 2002;

Maupin, 1993; Sutherland, 2009), and so, many youths may continue in the juvenile justice system without their needs being addressed (Vieira et al., 2009). For example, Flores and colleagues (2004) found that only 56.7% of practitioners utilized needs scores to ascertain treatment goals (p. 38). Additionally, they discovered that many interventions listed in youths’ case plans were not related to their actual needs. Bonta, Rugge, Scott, Bourgon, and Yessine

(2008) also found that criminogenic needs were relatively unaddressed by probation officers.

Furthermore, Haqanee, Peterson-Badali, and Skilling (2015) assessed whether youth probation officers addressed offenders’ needs; they revealed that probation officers frequently focused on non-criminogenic needs, including MHPs, and their participants felt that including such needs in treatment impacted youths’ responsivity and resulting success with services.

In practice, non-criminogenic needs appear to be targeted, but these same needs tend to be sacrificed in the RNR framework, suggesting criminogenic needs should be the focus of interventions (Robertson, Barnao, & Ward, 2011). For instance, although mental health issues do not have a direct effect on recidivism, as a responsivity factor, they can impact whether offenders effectively respond to treatment (Bonta, 1995). However, there is a lack of research regarding

how youth with MHPs respond to programming (Haqanee et al., 2015). In short, more information is needed regarding how MHPs impact the relationship between programming and reoffending. By understanding how these issues affect predictors of youth offending, specific responsivity, which is key to rehabilitation (Bonta & Andrews, 2017), may be better addressed, potentially resulting in youth offenders receiving treatment that corresponds with their risk, needs, and individual characteristics. In turn, individualized treatment matching may improve how youth respond to programming, potentially reducing their likelihood of reoffending.

Needs and the Life Course

Life course theories are pertinent to a discussion of RNR, as well as mental health concerns, as offending and mental wellness can change as youth develop. Needs alter an individual’s development and relationships with his or her social groups change across the life course (Laub & Sampson, 2003; Loeber et al., 2012; Piquero, Farrington, & Blumstein, 2003).

Yet, there is a lack of research examining how offender needs change over time (Taxman &

Caudy, 2015), and in regard to needs-service matching (Baglivio & Epps, 2016).

For instance, youth who commit crime earlier in life go on to have more severe needs and longer criminal careers (Blumstein, Cohen, Roth, & Visher, 1986; Farrington, 2006; Moffitt,

1993). Such offenders, who are considered high-risk, require the most intense rehabilitation

(Bonta & Andrews, 2017). Lifetime offending appears to be related to neurodevelopmental issues (Fairchild, Van Goozen, Calder, & Goodyer, 2013), poor family functioning (Moffitt,

2006), and trauma (Baglivio, Wolff, Piquero, & Epps, 2015) in childhood and adolescence.

Research indicates that these needs earlier in life contribute to needs (e.g., MHPs; Davis, &

Kendler, 1997; Green et al., 2010; McLaughlin et al., 2012) and offending later in life if they go untreated. For example, people with an earlier onset of deviant behavior have higher rates of

mental health concerns (Aguilar, Sroufe, Egeland, & Carlson, 2000; Ruchkin, Koposov,

Vermeiren, & Schwab-Stone, 2003). More empirical research is warranted regarding youth programming that addresses changing needs and whether youth are responsive to such services.

Adverse Childhood Experiences

One set of particular, non-criminogenic needs resulting in profound consequences for justice-involved youth involves Adverse Childhood Experiences (ACEs), which can also be conceptualized as a measure of trauma. Felitti and colleagues (1998) identified ten childhood experiences as risk factors for later physical and socioemotional negative outcomes, including three measures of abuse (physical, sexual, and emotional), two measures of neglect (physical and emotional), and five measures of household dysfunction (violence towards a child’s mother, parental separation or divorce, substance use, MHPs, and incarceration of a household member).

Research indicates that ACEs constitute one of the most damaging effects on development

(Kilpatrick, Saunders, & Smith, 2003; Widom, 2000), and they are more prevalent in youthful offenders than they are in non-youth offenders (Baglivio et al., 2014; Burke, Hellman, Scott,

Weems, & Carrion, 2011; Cronholm et al., 2015; Flaherty et al., 2009). There is also a relationship between ACEs and development of psychological concerns (Kessler, Davis, &

Kendler, 1997; Green et al., 2010; McLaughlin et al., 2012).

ACEs are critical to understanding youth recidivism and MHPs. Past research has shown that ACEs are related to offending and reoffending (Baglivio et al., 2014; Baglivio et al., 2015;

Barrett et al., 2014a & 2014b; Dembo et al. 1995; Dembo, Williams, Schmeidler, & Christensen,

1993; Maxfield & Widom, 1996; Rivera & Widom, 1990; Teague, Mazerolle, Legosz, &

Sanderson, 2008; Wolff & Baglivio, 2017), but the effect ACEs have on the association between programming and reoffending has not been explored for either offenders with mental health

issues or those without such concerns. If ACEs are not addressed through effective services, justice-involved youth may carry their trauma and/or MHPs into adulthood, which may result in a public health crisis and an economic burden on the community (Baglivio & Epps, 2016). The present study contends that, to improve intervention in and possible prevention of future offending, practitioners should consider ACEs as needs to be addressed when providing youth offenders with programming, particularly for youth with MHPs.

Youth with Mental Health Issues

Approximately 20% of youth in the United States are diagnosed with a mental health issue at some point during their life, and 40% of these youth report co-occurring MHPs

(Merikangas et al., 2010, p. 987). If not treated, some of these youth go on to have adverse outcomes in adulthood. Importantly, youth suffering from a MHP may cope with their symptoms via substance use and are also at risk for becoming involved in the juvenile justice system. For instance, nearly 70% of youth in the juvenile justice system report a diagnosable mental health disorder (Wasserman, Ko, Larkin, & McReynolds, 2004, p. 3), and 20% have a serious mental health issue (Cocozza & Skowyra, 2000, p. 6). In 2016, about 856,130 youth were arrested

(Office of Juvenile Justice Delinquency Prevention [OJJDP], 2017). If we extrapolate from these statistics, it is evident that MHPs are prevalent in justice-involved youth (Teplin, Abram,

McClelland, Dulcan, & Mericle, 2002; Vermeiron, 2003; Wasserman et al., 2004).

In fact, more than half of youth in correctional facilities have a history of mental health concerns (Otto, Greenstein, Johnson, & Friedman, 1992; Teplin, Abram, McClelland, Washburn,

& Pikus, 2005). Yet, many justice-involved youths do not receive treatment for their mental health and/or substance abuse issues (Skowyra & Cocozza, 2007) because the juvenile justice system is not equipped to address mental health needs. As a consequence, many youths’

symptoms may be undetected or untreated (Farmer, Burns, Phillips, Angold, & Costello, 2003;

Rogers, Pumariega, Atkins, & Cuffe, 2006). Unfortunately, mental health issues are both socially and economically costly. In 2009 alone, the United States spent $147 billion on mental health care (Substance Abuse and Mental Health Services Administration, 2013, p. 17). Through identification and intervention, we may be able to reduce these costs.

Still, even if MHPs are acknowledged as a concern for justice-involved youth, offenders with mental health concerns are often discussed as a homogeneous subset. There is a vast array of mental disorders that can broadly be categorized into internalizing (INT) and externalizing

(EXT) symptoms (Cicchetti & Toth, 1991). Yet, most research focuses on EXT disorders

(Achenbach, 1985; Achenbach & Edelbrock, 1981; Robins, 1966; Sroufe & Rutter, 1984). Past research has demonstrated that certain diagnoses, such as attention-hyperactivity/deficit disorder

(ADHD), predict recidivism and are more prevalent in chronic, persistent offenders (DeLisi,

Neppl, Lohman, Vaughn, & Shook, 2013; Hoeve et al., 2015; Moffitt, 1993; Wolff & Baglivio,

2017). Learning disabilities, and emotional/behavioral disorders (Barrett, Katsiyannis, Zhang, &

Zhang, 2014a; Wolff & Baglivio, 2017) are also common in offenders. However, Wolff and

Baglivio (2017) did not find that a mental health diagnosis was predictive of recidivism; yet, their measure did not include oppositional defiant disorder (ODD) or conduct disorder (CD).

Other researchers have shown that EXT disorders, including ODD and CD, are predictive of future criminality (Barrett et al., 2014a; Beauchaine, Klein, Crowell, Derbidge, & Gatzke-Kopp,

2009; Farrington, Ohlin, & Wilson, 1986). Accordingly, it is essential to assess specific diagnoses and their association with reoffending, rather than treating youth with MHPs as a homogenous subset whose symptoms manifest similarly. The current study addresses this need by examining the differential impact of INT and EXT disorders on youth recidivism.

Furthermore, subsets of youth offenders may differ in terms of what risk factors are likely to lead to reoffending (Moffitt, 1993, 2006). Accordingly, what may predict recidivism for youth without MHPs may differ from youth with such issues. Mental health concerns have been associated with an earlier onset of offending, as well as persistent offending (Moffitt, 1993,

2006), but the effect of mental health issues, especially different types of disorders, on the relationship between programming and recidivism has scarcely been examined. However, past research has indicated that practitioners must exercise caution when deciding on an intervention.

Again, the type of MHP is relevant, as some forms of psychological treatment can cause more harm than good. For instance, youth who experience EXT symptoms and who receive group treatment may become more antisocial compared to similar youth with no treatment because their behavior is positively reinforced by antisocial peers (Dishion & Racer, 2013). Accordingly, programming for certain youth may be iatrogenic. Instead of focusing on just risk or just needs, more attention should be given to youths’ responsivity to interventions, as youth characteristics may impact the efficaciousness of treatment. If responsivity is not considered, then we may garner more harm than good for both the individual youth, as well as, the community at large.

In sum, although MHPs and ACEs are not considered to be criminogenic needs (Bonta &

Andrews, 2017), the above evidence suggests they increase the likelihood of youth encountering the juvenile justice system (Dembo, 1996; Grisso, 1999; Wasserman et al., 2004), as many justice-involved youth present with mental disorders (Teplin et al., 2002; Vermeiron, 2003;

Wasserman et al., 2004) or trauma (Baglivio et al., 2014; Burke et al., 2011; Cronholm et al.,

2015; Flaherty et al., 2009). The above research would indicate that experiences with trauma and mental health issues are important and potentially present as a public health crisis (Baglivio &

Epps, 2016), as many youths who experience either or both have contact with the juvenile justice

system and may continue to offend if these needs are not met. Accordingly, without identifying these youth and providing them with effective interventions, they may go on to commit more serious offenses as adults. Furthermore, if we address trauma and MHPs, we may be able to shift from a reactive position to one of prevention by effective need-service matching.

Purpose of the Study

The current study proposes that, in order to improve prevention and intervention, ACEs and MHPs contribute to needs (e.g., substance abuse to cope with trauma or symptoms of a mental health concern) to target in programming, but they also present as potential responsivity factors that impact the effect of programming on recidivism. As such, the present study examines whether need-service matching decreases recidivism risk in a sample of youth offenders from

Washington State and whether programs contribute to recidivism reductions for youth with trauma histories and/or MHPs. Throughout this analysis, the relationship between ACEs, mental health issues, onset of deviance, and recidivism are also assessed.

The study first identifies youth needs via the PACT to determine groups of youth who:

(1) had no needs and no programming; (2) no needs but who received programming; (3) needs and programming matching needs; and (4) a programming mismatch (i.e. the intervention did not match their needs) to identify which group possesses the lowest recidivism rate. The effect of mental health concerns between male and female reoffending is examined in addition to the impact of ACEs on recidivism. The relationship between ACEs and reoffending via programming is further explored. The study then examines the differential impact of programming on recidivism for youth without mental health issues and those with such issues.

Differences between youth with an early or late onset of deviance are also tested.

Chapter two examines the RNR model and RNAs in more detail to demonstrate how risk, needs, and responsivity are used in practice. The PACT is highlighted in this discussion, as it is used in a variety of states (Baglivio, 2009) and is the youth assessment used in Washington State.

Life course theories, such as Moffitt’s (1993) dual taxonomy of youth offending, are also emphasized to show how different factors contribute to youths’ continued deviant behavior or desistance from crime. Furthermore, current research conducted on ACEs is included to illustrate the severe effects trauma in childhood can have on youth offending. Next, mental health issues within justice-involved youth are discussed to show how prevalent MHPs are in the juvenile justice system (Teplin et al., 2002; Vermeiron, 2003; Wasserman et al., 2004) and to better establish how mental health concerns constitute a potential need and responsivity factor that influences youths’ successful desistance from recidivism. This section is then broken down into

INT and EXT mental disorders, as criminological research has generally failed to examine how different types of mental disorders, with the exception of ODD and CD (Farrington et al., 1986;

Beauchaine et al., 2009; Barrett et al., 2014a), impact youth recidivism. Finally, different types of programs for youth are reviewed, including Family Functional Therapy (FFT), Multisystemic

Therapy (MST), and Aggression Replacement Therapy (ART), as these are programs utilized in

Washington State for youth offenders and will be examined as part of the current study.

Chapter three provides a description of the current study’s design and implementation.

The data includes youths’ scores on the PACT assessment, as well as arrest and disposition records and programming information. The analysis plan is also outlined in this section. Chapter four presents the outcomes from the analyses while chapter five provides an in-depth discussion of the findings. This section further includes directions for future work, limitations of the present study, and implications for policies involving youth offenders in Washington State.

CHAPTER TWO

REVIEW OF THE LITERATURE

The juvenile justice system originally focused on individualization of treatment (Bernard

& Kurlychek, 2010; Clarke, 2005; Rothman, 2012), but this objective is not easily achieved, as youth are in a transition period. Their risks and needs, as they pertain to offending, are also in flux (Laub & Sampson, 2003; Loeber et al., 2012; Piquero et al., 2003). Change over time is acknowledged in both life course theories and the RNR model.

Yet, youth rehabilitation, based on their specific needs, can sometimes conflict with protecting communities. Additionally, it can be difficult to effectively implement rehabilitative programming in correctional settings, particularly if the correctional organization is in disarray and/or violence is the norm (Dilulio, 1987). Regardless, instead of tailored treatment, the juvenile justice system often implements a one-size-fits all approach that is more punitive than it is rehabilitative (Redding et al., 2005). Moreover, treatment based on youths’ individual needs and responsivity has become less important (Feld, 1997).

Such individual needs and responsivity include ACEs and mental health issues. Even among youth who have MHPs, there is a distinction between INT and EXT symptoms (Cicchetti

& Toth, 1991) and behaviors associated with these syndromes may differentially impact recidivism. The effectiveness of risk assessments and different programming for these types of youth have not been directly examined and could be considered within the framework of life course theories and the RNR model. Not only are trauma and mental health concerns potential contributors to criminogenic needs (e.g., substance use), but they also impact programming effectiveness. The basic tenets of life course theory and the RNR framework, as well as the

difficulties that youth with a history of trauma and/or MHPs face in the juvenile justice system, were briefly discussed in Chapter One. Greater attention is given to these topics in this section.

Life Course Theory

A core principle in the juvenile justice system is that youth are developmentally different from adults and can be rehabilitated (Cullen & Wright, 2002; Empey, 1978; Platt, 1977;

Rothman, 2012). This belief is particularly relevant to life course theories and a developmental perspective. These approaches are dynamic, as they focus on how a person’s behavior is stable or changes across the lifespan (Farrington & Loeber, 2013; Loeber & Le Blanc, 1990; Loeber &

Stouthamer-Loeber, 1996). Additionally, since crime participation is highest when offenders are

17 or 18 (Agnew & Brezina, 2012; Caspi & Moffitt, 1995) and the majority of youth desist from crime during their 20s (Caspi & Moffitt, 1995), an examination of continuity or change in youth offending is best viewed through the lens of life course theory.

Following life course theories, an early onset of antisocial behavior has been associated with criminal behavior in adulthood (Farrington, 2006; Moffitt, 1993). Glueck and Glueck’s

(1950) seminal work established a foundation for researching youth and examining differences between youth who commit crimes and those who do not. Their work demonstrated that antisocial behavior was related to later criminal offending, and that this behavior was relatively stable from adolescence to early adulthood. Hence, crime is dynamic; life and social circumstances affect youth offending. This research contributed to three lines of life course theories: continuity, continuity or change, and continuity and change.

Criminal engagement may be stable, or have continuity, as a result of inherent traits. One example is Gottfredson and Hirschi’s (1990) self-control theory; where, once low self-control emerges, youth are on a pathway to criminality. In contrast, Moffitt (1993) developed a

taxonomy of youth offending that explains why crime appears to be high among adolescents, broadly defining two types of youth: life-course-persistent (LCP) and adolescent-limited (AL) offenders. LCPs are continuous in their offending, while ALs desist in their deviant behavior once they are adults. For LCPs, antisocial behavior is established at an early age, resulting from neuropsychological deficits and other individual traits linked to misconduct. As a consequence of these deficits, LCPs experience a series of failures, poor choices, and lost opportunities that limit their ability to change. In contrast, the onset of offending for ALs is largely the result of a maturity gap, where youth are starting to more physically and biologically resemble adults while at the same time they are expected to abstain from adult behaviors. These youth become dissatisfied with this mismatch between their developmental change and societal expectations, which results in motivation for delinquent behavior. Yet, as ALs age, and social convention allows them to act more like adults, the motivation for delinquency desists. In short, Moffitt contends that, although most youth engage in deviant behavior, only a small percentage (5%) will continue to commit crimes as adults (p. 677).

Sampson and Laub (1990, 1993) also endorse a life course perspective, but their theory focuses on an individual’s ability to be both continuous and changeable in his or her behavior.

They describe trajectories and transitions in life. Trajectories constitute a sequence of life events within shorter time spans (i.e. getting married) where transitions may result in a person’s social identity or status changing. These turning points can help a youth desist from crime, and there is a strong association between events that occur during childhood and experiences in adulthood.

Yet, turning points can redirect a person’s trajectory, and thus constitute a change in that path.

Laub and Sampson (2003) later incorporated human agency into their theory, describing how

offenders can act to desist or persist in crime when turning points occur. This decision results in a change or continuity in offending in that moment.

Life course theories are thus interrelated with human development, where lifetime offending is related to neurodevelopmental issues (Fairchild et al., 2013), as well as a poor family functioning and a greater likelihood of serious, violent offending (Hoeve et al., 2008;

Moffitt, 2006). Additionally, people with an earlier onset of deviant behavior have higher rates of MHPs. For incarcerated youth specifically, those with an earlier onset have higher rates of

EXT disorders, posttraumatic stress disorder (PTSD), and depression (Ruchkin et al., 2003). In contrast, Aguilar et al. (2000) found that youth with a late onset evidence more stress and INT behaviors. Accordingly, age of onset may be related to the types of MHPs youth display.

Life course theories are pertinent to the current study. As suggested by Moffit (1993) and

Sampson and Laub (1990, 1993), certain experiences early in life may contribute to long-term offending. ACEs and mental health issues may constitute substantial events for youth that impact the decision about whether they continued to engage in offending. The RNR model can further clarify how these experiences contribute to offending.

Risk-Need-Responsivity Model

The role of actuarial sciences within the juvenile justice system has expanded, and actuarial science, coupled with the RNR model, has provided scholars and practitioners with the requisite tools to better classify offenders by risk (Bonta & Andrews, 2017). Past scholars have documented the efficacy of RNR when implemented with integrity (Andrews & Dowden, 2005).

In turn, practitioners can now provide services that better meet the needs of justice-involved youth. While the RNR framework has been applied to the adult system, it has not been fully

established or well-researched in the juvenile justice system (Singh et al., 2014). A discussion of the RNR principles will better elucidate how this framework is applicable to youth offenders.

The risk principle pertains to likelihood of reoffending (Bonta & Andrews, 2017). The more risk factors offenders have, the higher their risk. Specifically, treatment and programming resources should be reserved for high-risk offenders because targeting low-risk offenders can be detrimental, as they may be exposed to conditions (e.g., more antisocial peers) that negatively affect their risk to reoffend and may be removed from their support system (Andrews &

Dowden, 2006).

Risk factors can be changeable (dynamic; e.g., association with antisocial peers) or static

(e.g., criminal history). Both types of risk can be assessed to reduce recidivism. Vincent, Guy, and Grisso (2012) discovered that the risk principle was inconsistently applied; instead, detention decisions were based more on offense history and the judge. Stated otherwise, judges may not consider youths’ identified risk when making decisions regarding dispositions.

The need principle focuses on potential causes of offending, and which criminogenic needs should be targeted to reduce criminal behavior (Bonta & Andrews, 2017). Examples of criminogenic needs include antisocial peers, substance abuse, and antisocial attitudes. Needs and risks can be confused, as some risks (i.e. substance use) can also be considered needs. Non- criminogenic needs, such as stress, trauma, and MHPs, are not typically targeted to improve offenders’ desistance from crime. Furthermore, a mismatch between youths’ needs and programming may have a detrimental effect on youth, as youth who complete treatment not specific to their needs may spend more time in the juvenile justice system, resulting in greater exposure to antisocial peers and an increased likelihood of recidivism (Vincent, 2011).

The responsivity principle covers the types of services that should be provided to an offender as well as how those interventions should be delivered (Bonta & Andrews, 2017).

Responsivity factors assist in treatment planning because they involve offenders’ personal characteristics (i.e. strengths and learning style). General responsivity refers to programming types, like cognitive-behavioral therapy, while specific responsivity involves offender’s individual problems that may inhibit program participation and completion (Bonta & Andrews,

2007). Different learning styles, anxiety, motivation, culture, and gender constitute examples of specific responsivity (Cullen, 2002). Responsivity is often excluded from analyses of interventions (Hubbard, 2007; Kennedy, 2000; Nee et al., 2012). However, treatments may be more effective if they are relevant to offenders (Cann, 2006; McMurran & McCulloch, 2007;

Vieira et al., 2009) and are tailored to offenders’ needs (Kazdin, 2008; Vieira et al., 2009).

In short, the RNR model is intended to inform the rehabilitative aim of corrections, and assessment of risk is meant to identify appropriate interventions rather than inform surveillance and/or control strategies (Viglione & Taxman, 2018). Yet, risk is frequently utilized to serve this latter purpose, which aligns with a public safety perspective (Viglione, Rudes, & Taxman, 2015).

A consequence of this approach is that higher risk individuals may receive stricter supervision, potentially increasing their likelihood of a technical violation and resulting incarceration while having no effect their rates of offending (Hyatt & Barnes, 2017). Conversely, low-risk individuals may still have criminogenic needs that should be addressed with programming to decrease their risk of continued criminal justice involvement (Taxman & Caudy, 2015); however, this notion is at odds with the risk principle. Consequently, there is tension amongst the principles; yet, as described by Andrews and Dowden (2005), proper implementation of these principles contributes to reduced recidivism, and they have been applied to risk assessments.

Risk Instruments

Managing offenders through face-to-face interactions and professional instinct (Rothman,

2012) has given way to using risk assessments within corrections (Bonta & Andrews, 2017).

Risk instruments offer a standardized method to collect data that can then be used to make decisions about resource allocation (Shook & Sarri, 2007). As a result of standardization, court procedures and punishment are, theoretically, more fair.

Evolution of risk assessments. Use of scientific risk tools has evolved through a generational trajectory, where each subsequent development has offered a new contribution to instrument utility (Schwalbe, 2007). However, new developments do not necessarily improve an assessment’s predictive strength (Hamilton et al., 2016); yet, changes can increase a tool’s utility. First generation assessments were characterized by practitioner judgement (Schwalbe,

2007; Schwalbe, Fraser, Day, & Arnold, 2004). Compared to other generations, first generation tools have weak predictive validity (Andrews et al., 2006). Moreover, professionals’ predictions regarding offenders’ future violence, based on their experience, are accurate in about one-third of cases (Grisso & Tomkins, 1996). More objective assessments, based on actuarial methods, were created in the late 1920s (Burgess, 1928), but practitioner judgement was not replaced by second generation instruments (static risk assessments) until the 1970s (Bonta & Andrews, 2007).

Second generation instruments introduced statistical indexes and analyses of static risk factors, such as criminal history (Schwalbe, 2007). This addition allowed practitioners to implement risk-appropriate case and supervision planning. The transition to second generation risk tools has been empirically supported, as statically driven instruments better typically predict risk than practitioner judgement (Brennan, Dieterich, & Ehret, 2009; Schwalbe et al., 2004).

Third generation assessments were developed following growing support for the RNR model. These assessments include both static and dynamic risk factors (Bonta & Andrews,

2017), which allows for improved predictive validity and utility (Jung & Rawana, 1999; Loeber

& Farrington, 1998) as well as the establishment of more precise individualized interventions

(Schwalbe, 2008). Moreover, Cottle, Lee, and Heilbrun (2001) discovered that certain static factors (out-of-home placements, living in a single parent household, and a history of abuse) increased predictive strength. However, once these items were isolated, the presence of static and dynamic factors provided an optimal assessment of criminogenic trajectories. Vincent, Chapman, and Cook (2011) discovered that inclusion of static and dynamic variables in the Structured

Assessment of Violence Risk in Youth (SAVRY) predicted non-violent and violent recidivism.

This finding demonstrates how risk assessments can be used to predict multiple outcomes.

Fourth generation assessments include static, dynamic, and protective factors; as such, these instruments are considered responsive (Andrews et al., 2006). Inclusion of these factors allows for an evaluation of an offender’s rehabilitative progress and examination of criminogenic needs over time (Brennan et al., 2009). As such, offenders who remain involved in the criminal justice system can be re-assessed to determine whether their needs have changed and whether programming they have been assigned to is effective for them. Fourth generation tools are also useful for case planning, as practitioners can respond to offenders’ individual learning styles and needs (Bonta & Andrews, 2017). Risk assessments that follow the RNR model may improve outcomes for youth, but controversy remains regarding the generalizability of these instruments.

Since youth are in a state of change, and recidivism risk can also fluctuate, it is critical to understand the predictive validity of risk instruments when determining a youth’s probability of reoffending and what programming the youth should receive (Baglivio & Jackowski, 2013).

Accurate classification by risk allows practitioners to reserve the most intensive programming for high-risk youth (Andrews et al., 2006; Schwalbe et al., 2004), which prevents low-risk youth from being exposed to high-risk peers. Such exposure may contribute to low-risk youth having a higher probability of recidivating (Gatti, Tremblay, & Vitaro, 2009). As suggested by Andrews and Bonta (2010), one way to improve placement and programming decisions for offenders is through the use of assessments.

Risk/Needs Assessments (RNAs). Risk tools predict the probability that an offender will commit a new offense (Vincent et al., 2012). Youth risk assessments typically include questions regarding youths’ prior offending history, as well as their personal background. Answers for each item are then tabulated and the youth is assigned a risk category (Moore & Padavic, 2011).

In contrast, needs assessments emphasize identification of interventions most appropriate for an offender to reduce recidivism risk (Andrews & Bonta, 2010). Responsivity assessments identify factors related to treatment effectiveness for individual offenders and provide recommendations regarding placements before programming (Van Voorhis, Braswell, & Lester, 2004). However, risk/needs assessments (RNAs) are far more common than responsivity tools.

Juvenile justice systems in most states have implemented RNAs (Wachter, 2015) to guide supervision level, case management, and programming (Andrews et al, 2006; Taxman et al.,

2004). Validated assessments utilized to guide programming decisions for youth in the juvenile justice system are considered a best practice (Singh et al., 2014; Vincent et al., 2012), as these tools offer a scientific approach to gauge offenders’ probability of reoffending (Funk, 1999).

However, as discussed by Schwalbe (2004), juvenile justice practitioners may underutilize RNAs or disregard risk classifications and/or needs assessment information.

Furthermore, concern exists regarding the use of only initial risk scores to predict the probability of reoffending, as recidivism risk is dynamic (Greiner, Law, & Brown, 2014). As such, assessments should be re-administered across time (Howard & Dixon, 2013). Continued assessment is particularly relevant for youth offenders because their development is in flux, youth have a shorter history of deviant behavior, and they have different behavioral norms than adults (Borum, 2000; Moffit, 1993); thus, their scores may ‘expire’. For instance, Barnes et al.

(2016) assessed the predictive accuracy of initial, exit, and change in risk instrument scores for

360 youth under probation to analyze the predictive validity of change in risk. Exit and change better predicted recidivism than initial risk scores. Therefore, an assessment of risk over time improves predictive accuracy of reoffending.

Implementation and adherence. Despite wide use of such assessments (Wachter, 2015), concerns remain regarding the validity and reliability of these tools (National Council on Crime and Delinquency, 2014). This concern is especially relevant to the reliability of rating dynamic risk factors. Still, when the RNR model is adhered to, assessments and resulting programming can improve youth outcomes (Andrews & Dowden, 2006; Andrews et al., 1990; Dowden &

Andrews, 2004). For instance, accurate matches between youths’ risks or needs and intervention can reduce recidivism (Lipsey, 2009; Luong & Wormith, 2011; Baglivio et al., 2014). However,

Bonta et al. (2008) found a mismatch between identified needs in an assessment and case management decisions in a sample of youth and adult probationers in Manitoba, where 39.4% of identified needs had a corresponding plan to address probationers’ criminogenic needs (p. 259).

The authors discovered that more time spent on discussing criminogenic needs with probationers during supervision meetings was related to decreased recidivism. It should be noted that Bonta and colleagues (2008) had a small sample of probationers (n = 213), potentially limiting

generalizability of their findings. The above research suggests programs and supervision strategies that do not adhere to the RNR model may have either no effect or increase recidivism.

Even if risk tools are well-developed, they must be implemented effectively to ensure positive outcomes (Shook & Sarri, 2007). Little research has evaluated implementation of structured instruments in the juvenile justice system. For example, many jurisdictions have implemented risk instruments; yet, practitioners often do not know how to use the information derived from these assessments to successfully assist youth (Dedel, 2015).

Practitioners may also elect to forgo assessments that are well-developed. As an example,

Shook and Sarri (2007) studied 12 juvenile justice courts and discovered that structured decision- making was inconsistently and non-systematically implemented in case processing. Decision- making was further hindered by court officials’ disagreement with assessment recommendations, a lack of training, and/or limited treatment options and resources. These barriers resulted in judges and probation officers overriding tool recommendations. Some professionals also resented that their discretion was replaced by a tool, and others found the tools to be too time consuming, cumbersome, and difficult to use. Accordingly, organizational choices and practices impact whether assessments are effective.

Even when assessments following the RNR model are used, the results may be poor because these instruments are often adopted from existing risk tools off-the-shelf that were originally created in another jurisdiction with a different sample of offenders (Hamilton et al.,

2016). Thus, risk assessments should be tailored to a jurisdiction’s characteristics and be evaluated for predictive accuracy by using local item selection, item weighting, and risk category

cut points that are jurisdiction-specific1. The recidivism outcome should also align with the jurisdiction’s definition of that outcome.

Thus, effective implementation of risk tools is essential, as bias in decision-making can be minimized, and agencies can have a common language to better appropriate resources to target youths’ needs (Vincent et al., 2012). In turn, communities can decrease spending by reducing the number of youths incarcerated or supervised in the community. Adherence to such tools can also impact reoffending.

For example, Baglivio et al. (2014) evaluated how adherence to a dispositional matrix in

Florida used to match youth offenders to services impacted youth recidivism. According to the matrix, low-risk youth should have limited supervision while moderate- and high-risk youth should have greater supervision. Baglivio and colleagues found that the least restrictive dispositions in the suggested range yielded the lowest reoffending rates (19.4%), but 38.7% of youth placed outside of the suggested range reoffended (p. 19). More restrictive placements in general resulted in higher recidivism rates; yet, youth who were placed in less restrictive settings than suggested had the highest recidivism rates. Therefore, failing to follow instrument suggestions may contribute to higher recidivism rates.

Need-Service Matching

A small portion of the highest risk offenders go on to commit approximately half of youth crime (Moffitt, 1993), and the costs associated with these offenders is substantial (Cohen

& Piquero, 2009); consequently, it is important to provide these offenders with effective treatment to prevent crime. Such treatment should match youths’ criminogenic needs (Lipsey,

Howell, Kelly, Chapman, & Carver, 2010). However, many studies that investigate the need

1 Discussed in more detail below.

principle focus on groups, rather than the individual (Nelson & Vincent, 2018). An individual approach to the need principle assesses the individual service-to-need matches. The few studies that have examined need-service matching within the juvenile justice setting have observed a relationship between need-service (mis)matches and deviant behavior (Luong & Wormith, 2011;

Peterson-Badali et al., 2015; Vieira et al., 2009).

For example, Luong and Wormith (2011) found that Canadian, high-risk youth on probation who had case plans matching their needs exhibited 48% reduced odds of recidivism, while an absence of those interventions resulted in 82% increased odds of reoffending (p. 1191).

Although need-service matching was related to recidivism, a notable limitation of this study concerns a lack of investigation regarding specific programming youth received. Additionally,

Baglivio, Wolff, Howell, Jackowski, and Greenwald (2018) found that interventions matching youths’ individual dynamic risk and needs, rather than global criminogenic needs, resulted in a

17% reduced recidivism rate compared to youth without matching needs and programming (p.

53). Moreover, youth with a need-service match had also met their target Standardized Program

Evaluation Protocol hours and weeks; such youth were matched to similar youth without these three components. Although Baglivio et al. (2018) found a significant difference between the two groups, these may be conservative estimates, as youth with just a need-service match (or only one or two of the other components) were included in the nontreatment group. Nelson and

Vincent (2018) also examined service-to-need matching in a sample of probation youth who were assessed via the SAVRY. The authors found that youths in their study had an average of

3.6 needs (p. 8) and only 1.7 needs addressed by one or more services (p. 9). Furthermore, youth’s risk level affected the number of service referrals they received. Specifically, low-risk youth received significantly fewer need-service matches than moderate- and high-risk youth.

Nelson and Vincent concluded that probation officers made better matches when needs were absent but still over-prescribed services for low-risk youth.

Therefore, matching offender needs with appropriate services can facilitate rehabilitation and reduce reoffending (Andrews & Dowden, 2006; Andrews et al., 1990; Dowden & Andrews,

2004; Peterson-Badali et al., 2015; Vieira et al., 2009; Vitopoulos et al., 2012). Yet, identified criminogenic needs may not be considered in service planning (Flores et al., 2004; Latessa et al.,

2002; Maupin, 1993; Sutherland, 2009), and many youths’ needs are not addressed (Vieira et al.,

2009). Flores et al. (2004) reported that many of the correctional staff they surveyed did not utilize criminogenic need scores to establish treatment goals; moreover, services provided were often not related to identified needs. Furthermore, Peterson-Badali et al. (2015) found that an average of 1.4 identified needs were addressed by programming and 40% of probation youth had needs that went unaddressed (p. 313). Youth with a need-program match also had decreased recidivism compared to youths with mismatches. Some of the authors’ rationale for why needs were unaddressed included: insufficient resources (e.g., few services address antisocial attitudes and/or antisocial peers); lack of youth motivation (Haqanee et al., 2015); family problems (e.g., parental mental health concerns); community level issues (e.g., unsafe neighborhoods); prioritization of non-criminogenic needs (e.g., MHPs) to facilitate stability so a given youth is more receptive to other programming; and focusing on certain criminogenic needs, rather than all, to help youth achieve some change.

Additionally, Vieira et al. (2009) studied a sample of 122 youth referred for an assessment to a mental health agency in Ontario between 2001 and 2005. Youth were administered the YLS/CMI to assess criminogenic needs, and the authors revealed that youth who had fewer needs targeted by programming reoffended more quickly and were more likely to

recidivate than youth who had more need-service matching. One limitation of this study regards the lack of a comparison group of non-referred youth to investigate whether need-service matching would impact their reoffending. Also, and similar to Luong and Wormith (2011), the authors were unable to measure individual programs, including the quality of the program and its staff, or whether multiple programs targeted the same need.

Responsivity may also go unaddressed. In a sample of probation youth, Luong and

Wormith (2011) found that responsivity factors were not identified in 80% of youth (p. 1189) and commented that the responsivity principle may not have a role in treatment decisions. Yet, matching interventions to youths’ needs and responsivity can preserve resources for youth with greater needs (Vieira et al., 2009) while ensuring that low-risk youth have less exposure to the juvenile justice system and antisocial peers (Dishion & Tipsord, 2011; Gatti et al., 2009).

Moreover, a hyper-focus on risk may not be beneficial as too much focus on risk can lead to more restrictive punishment than is needed, and adherence to the need and responsivity principles has also been associated with positive effects (Singh et al., 2014).

Non-criminogenic needs are also important. Crites and Taxman (2013) suggest that such needs should be addressed, as they may improve treatment effects. However, non-criminogenic needs are not emphasized within the RNR framework (Robertson et al., 2011), but they may be treated as responsivity factors. For instance, although MHPs are not considered criminogenic, they may impact how an offender responds to treatment for substance abuse (Osher et al., 2012).

Still, mental health issues remain under-researched as a responsivity factor (Haqanee et al.,

2015), despite it potentially interfering with treatment success (Andrews & Bonta, 2010).

Generalizability of Assessments

Risk instruments follow a diagnostic approach. A person’s individual scores are summed until a threshold is reached and exceeded; at that point, a risk level can be assigned. One assumption in this approach is that the outcome is the same for everyone (Desmarais & Singh,

2013). This supposition is problematic when recidivism is the outcome, as jurisdictional variations in laws and offender populations alters how much each item should be weighted in a prediction equation.

Not only are jurisdictional differences important, but so too are gender2 and racial/ethnic variations. One goal of risk assessments is to lessen gender and racial/ethnic biases (Shook &

Sarri, 2007). Much of the risk assessment research has been based on White males (Hoge,

Vincent, & Guy, 2013); hence, information from risk assessments may not be reliable for females and/or racial/ethnic minorities. In fact, inconsistent predictive validity for youth risk assessments has been found for different samples of offenders (Andrews et al., 2011;

Gottfredson & Moriarty, 2006). However, other researchers have suggested that some risk assessments are generalizable across different populations (Bonta & Andrews, 2017). Therefore, it is unclear whether all risk instruments are valid across gender and racial/ethnic lines, necessitating validation studies with different populations.

Racial/ethnic bias. Although risk assessments may be able to lessen racial/ethnic disparities by providing a uniform method to analyze youth, these tools may also increase differential treatment because some risk items are correlated with race/ethnicity, such as poverty, poor schooling and neighborhood conditions (Mulvey & Iselin, 2008). These social circumstances can result in more exposure to the police and criminally engaged youth,

2 Gender differences in risk assessments are discussed more in-depth in the section entitled “Gender Responsivity”

potentially resulting in an increased likelihood of deviant behavior. As such, one concern is the potential for racial/ethnic bias in risk instruments, as marginalized youth may receive higher risk scores as a result of being exposed to greater risk and social inequality rather than an actual greater propensity for committing an offense (Hannah-Moffat & Maurutto, 2010; Tonry, 2014).

Also, minority youth are disproportionately represented in the juvenile justice system at every decision-making point, and this disadvantage may accumulate (e.g., cumulative disadvantage) as they continue to move through the system (National Council on Crime and Delinquency, 2007;

Puzzanchera, Adams, & Hockenberry, 2012). Essentially, cumulative disadvantage may affect risk classification; in turn, decisions resulting from a youth’s given risk category may be the result of social circumstances contributing to the youth’s appearance in the juvenile justice system rather than the youth being an actual threat to his or her community.

In terms of dynamic risk factors, racial/ethnic bias may be present because these items can be experienced differently or have disparate impacts as a function of race/ethnicity or gender

(Hannah-Moffat, 2012). Higher risk scores may also result in racial/ethnic minorities receiving harsher punishments (Moore & Padavic, 2011), rather than treatment or other programming. As such, racial/ethnic minorities may not receive as much programming for their specific needs.

Jurisdictional variations. Risk tools may have decreased predictive accuracy when adopted off-the-shelf and implemented elsewhere (Barnoski & Drake, 2007), as differing laws, demographic characteristics, programming capacity, and practices across jurisdictions affect predictive accuracy (Hamilton et al., 2017). Therefore, risk category thresholds, selecting items that best predict recidivism for a given jurisdiction’s population, and optimizing response weights can improve instrument accuracy (Hamilton et al., 2016; Hamilton et al., 2017). In other words, instruments can be tailored for specific jurisdictions by selecting and weighting items that

best suit an agency’s statutes, definitions, and offender population. Items that are not strong predictors of recidivism can also be removed, and the predictive performance of the tool can then be improved (Hamilton et al., 2017).

Risk assessments should also be reviewed periodically, as predictive accuracy can change over time and across places (Barnoski, 2004a); thus, they should be updated as needed to ensure acceptable predictive performance. Offenders also change over time, so revalidation can confirm or disconfirm whether the instrument still has good predictive validity. Probabilities associated with risk factors change over time as well, and some risk factors may become less important in predicting recidivism than they previously were (Dean & Duggan, 1968). Hence, gender responsivity, racial bias, item selection and weights, multiple outcomes, and prediction duration should be assessed by jurisdiction and within jurisdictions over time.

Item selection and weighting. Included items in risk tools should be empirically related to the outcome of interest (Andrews & Bonta, 2010). Theoretically irrelevant items cause prediction noise, lack face validity, and affect an instrument’s predictive accuracy (Baird, 2009).

The associative strength between an item and the outcome can be determined by less stringent, statistically significant bivariate relationships, as is done with the YLS/CMI. One limitation of this approach is that items may be included that are not as strongly related to the outcome of interest, which results in less attention directed towards items that are more predictive.

Multivariate models, which are used in the Youth Correctional Offender Management Profile for

Alternative Sanctions (COMPAS), offer a stricter criterion for item inclusion (Blomberg, Bales,

Mann, Meldrum, & Nedelec, 2010). This latter approach also decreases concerns related to shared variance and multicollinearity.

Most risk instruments implement Burgess or unweighted scoring to determine item weights (Hamilton et al., 2016). With these approaches, each item’s ability to predict reoffending is treated equally when computing risk scores. However, predictive accuracy can be improved by assigning points to items proportionate to their ability to predict recidivism. This process, termed analytic weighting, helps illuminate item-level importance in predicting reoffending.

Outcome specificity. Risk instruments typically examine one recidivism outcome (‘any’ recidivism) by using a single model to predict risk. Hence, they do not account for varying crime types. This method can be limiting as different agencies may be interested in predicting recidivism for different types of crime, such as violent, drug, or property crimes (Hemple, Buck,

Cima, & van Marle, 2013; Vincent et al., 2012). Multiple models can be created within an assessment to predict recidivism for specific crime types. This method is done by selecting and weighting items associated with each outcome. For instance, an item indicating past substance use would be given more weight if the desired outcome involves predicting future drug offenses.

Furthermore, the population being served impacts the outcome. For instance, recidivism for a youth probation population may involve a probation violation or new offense from the time of disposition start. In contrast, recidivism for incarcerated youth may involve a new offense once the youth has been released and is in the community (e.g., disposition end date).

Alternatively, jurisdictions may just be interested in probation/parole violations, new contact, and/or further adjudications. In short, the outcome a jurisdiction wishes to observe is essential to item selection and weighting so predictive accuracy can be improved.

Prediction duration. Agencies may also vary in their follow-up time for recidivism. For example, one agency may examine recidivism within 12 months while others use a threshold of

24 or 36 months. Follow-up times should be considered when developing tools, or when

adopting them off-the-shelf, as the duration of recidivism impacts predictions models. Item selection and weights also vary depending on the follow-up period.

For instance, out-of-home placements may be more important in the short-term, as a youth may be placed in a foster or group home for 6 to 12 months and receive greater supervision. However, after 12 months, the youth may be back at home with less supervision.

Accordingly, current living situation may predict recidivism differentially across prediction duration times. Therefore, predictive performance may be improved by also considering differing follow-up periods across jurisdictions. Consideration of jurisdictional variations, including item selection and weighting, outcome specificity, and prediction duration, is essential for risk instruments used across state lines. One example of an assessment used across the United

Studies, and the focus of the current study, involves the PACT.

The Positive Achievement Change Tool

The PACT was initially developed as the Washington State Juvenile Court

Administrators Risk Assessment (WSJCA-RA) in Washington State in 1997 (Barnoski, 2004a), and it is one of the most commonly used youth RNA tools in the U.S. (OJJDP, 2010). The assessment was created following the Community Juvenile Accountability Act of 1997, which recommended that practitioners use intervention programs supported by research (Barnoski,

1999). The WSJCA-RA was created by the Washington State Juvenile Court Administrators

(WSJCA) and Washington State Institute for Public Policy (WSIPP) to identify risk and needs in probation youth (Barnoski, 1999). The tool was tested in 12 juvenile courts; in 1999, it was implemented statewide (Barnoski, 2004c).

The WSJCA-RA includes protective and responsivity factors; hence, it is a fourth- generation instrument used to assess youth offenders’ overall risk for recidivism and rank level

of needs. The assessment involves a semi-structured interview, where practitioners utilize

Motivational Interviewing to complete the tool with youth (Miller & Rollnick, 2002).

Two versions of the WSJCA-RA exist. The Pre-Screen has 46 items, and the Full

Assessment has 126 items across 12 domains (Barnoski, 2004a). These domains include:

Criminal History, Demographics, School, Use of Free Time, Employment, Relationships,

Family, Alcohol and Drugs, Mental Health, Attitudes/Behaviors, Aggression, and Skills. Each domain consists of a risk score, and many of them also have a protective score. Both versions provide the same overall risk classification (low, moderate, moderate-high, or high) and score for a youth. This score is derived from a matrix consisting of criminal history and social history sub- scores. Scores from the PACT are automated into a case plan, and youth are reassessed every six months, so practitioners may gauge a given youth’s rehabilitation. The criminal history sub-score

(amount and serious of previous crimes and placements) and social history sub-score

(environmental, family, and individual risk factors) are provided by both the Pre-Screen and Full

Assessment. Practitioners can learn more about a youth’s past experiences and current situation by administering the Full Assessment.

The WSJCA-RA has moderate predictive accuracy and has been adopted in other jurisdictions (Barnoski, 2004a). The WSJCA-RA was later rebranded by two private vendors and has been distributed widely across the U.S. (Baird et al., 2013). One private vender,

Assessment.com, developed a software application of the instrument, called Back on Track!

(Barnoski, 2004a). The tool was updated, renamed the PACT, and distributed to Delaware and

Florida’s department of juvenile justice. Orbis Partners (2000) also created a version of the

WSJCA-RA, called the Youth Assessment and Screening Instrument (YASI) and sold the instrument in New York. The YASI has also been widely distributed to several jurisdictions,

including one Canadian jurisdiction, Hawaii, Illinois, Fulton County, Mississippi, North Dakota,

San Francisco County, and Vermont. Other states have adapted the WSJCA-RA and made minor adjustments, resulting in Utah’s Protective and Risk Assessment (PRA), the Oregon Youth

Authority/Risk-Need Assessment (OYA/RNA), and the Iowa Delinquency Assessment (IDA).

The PACT, under multiple names and used in several states, has been essentially unchanged since it was originally implemented in Washington State. Furthermore, the PACT does not directly address a previous history of trauma; rather, items in it can be used to create an

ACEs score (Baglivio et al., 2014). As described above, trauma may be more critical to female offending (Hubbard & Pratt, 2002), so examining whether traumatic experiences impact risk prediction may improve utility of the PACT.

Adverse Childhood Experiences

Maltreatment of children is a substantial concern in the U.S. and constitutes one of the most damaging effects on child development (Kilpatrick et al., 2003; Widom, 2000). Results from the 2011/2012 National Survey of Children’s Health revealed that 46% (p. 1) of children experienced one or more ACEs, while 11% (p. 10) experienced three or more by the time they were 18 (Sacks, Murphey, & Moore, 2014). Although manty children are exposed to trauma

(Anda et al., 2006; Copeland, Keeler, Angold, & Costello, 2007), certain subsets of children are at a higher risk for experiencing adverse events, including justice-involved youth (Burke et al.,

2011; Baglivio et al., 2014; Cronholm et al., 2015; Flaherty et al., 2009).

Research has shown that the each of the individual ACEs identified by Felitti et al. (1998) negatively impact a person’s development (Anda, Butchart, Felitti, & Brown, 2010). Yet, ACEs frequently co-occur (Edwards, Holden, Felitti, & Anda, 2003; Finkelhor, Ormrod, & Turner,

2007), and exposure to several ACEs has an exponential impact, inflicting greater harm as the

number of ACEs increase (Cronholm et al., 2015; Edwards et al., 2003; Felitti et al., 1998;

Mersky, Topitzes, & Reynolds, 2013; Schilling, Aseltine, & Gore, 2007), indicating a dose- response effect (Anda et al., 2006; Anda et al., 2010; Dong et al., 2004; Felitti et al., 1998;

Flaherty et al., 2009). In other words, ACEs have a cumulative impact on development (Anda et al., 2010; Dong et al. 2004). Accordingly, examining ACEs individually undermines the interrelatedness of these experiences (Baglivio, Wolff, Epps, & Nelson, 2017).

To illustrate, Dong et al. (2004) found that of subjects who reported experiencing one

ACE, 86.5% had also experienced at least one more ACE, and 38.5% had experienced four or more ACEs (p. 776). Hence, ACEs should not be assessed as isolated occurrences, as exposure to one ACE increases the probability of being exposed to other ACEs. More recently, Baglivio and Epps (2016) discovered that a youth’s odds of having additional ACEs increases if he or she has experienced at least one ACE.

Theories Explaining ACEs

Multiple theories are relevant to ACEs. Life-course theories are particularly salient, as events in childhood are associated with experiences in adulthood, and criminal trajectories can be redirected by critical events (Sampson & Laub, 1990). Trauma can constitute a life-altering transition point. Accordingly, experiences of child maltreatment have negative consequences later in adolescence and adulthood (Anda et al., 2006, 2010).

Again, Moffitt’s (1993) taxonomy of LCP and AL youth is relevant. LCP offenders tend to have neuropsychological concerns that interact with their social environment, including experiences of maltreatment, that can contribute to offending across the lifetime. Moreover, LCP youth have genetic predispositions that prime them to be more sensitive to traumatic experiences

(Eley, Lichtenstein, & Moffitt, 2003; Hoeve, McReynolds, & Wasserman, 2015; Taylor, Iacono,

& McGue, 2000). Since these children tend to have more difficult temperaments and be more impulsive, parents have greater trouble raising them and providing adequate supervision and attention (Hertzig, 1983; Moffitt, 1993), resulting in disrupted neurological functioning (Duke,

Pettingell, McMorris, & Borowsky, 2010) and a risk for LCP offending. In fact, offenders who start criminal behavior early in life are more likely to report ACEs (Hoeve et al., 2015).

Furthermore, Baglivio et al. (2015) examined offending trajectories and discovered that youth with greater ACE exposure had a higher likelihood of being chronic offenders compared to youth with fewer ACEs.

Temperament may also affect responses to trauma. DeLisi and Vaughn (2014) discuss temperament-based theory, where effortful control (e.g., boldness, impulsivity, and low conscientiousness) and negative emotionality (e.g., hostility and anger) constitute part of self- regulation during childhood. When children fail to show control or display negative emotionality, parents tend to respond negatively, potentially resulting in an ACE. Children with more antisocial traits provoke more reactions from adults that result in ACEs, and we again see the interplay between genetic predispositions and social environment. In this case, this interaction impacts the child’s temperament, and a negative temperament may be associated with eventual involvement in the juvenile or criminal justice system.

General strain theory is also relevant. As Agnew (1992) suggested, there is a pathway from strain, to negative emotions, to offending. People commit crimes to cope with stress. One stressor that is likely to contribute to offending is childhood maltreatment, particularly if that stress is long-lasting and is not resolved (Agnew, 2001). Additionally, Agnew (1992) suggested that cumulative stress is more likely to result in offending. Consequently, multiple stressors, or

ACEs, have a greater likelihood of resulting in criminal behavior than just one or two.

Furthermore, social learning is germane to ACEs, as children may learn criminal offending from family members (Akers, 1985). Children exposed to criminal behavior may then mimic that behavior. As such, children who witness substance use, domestic violence, or who are victimized in the household may copy those behaviors.

Racial/Ethnic Differences in ACEs

Research on the prevalence rates of ACEs in minority and White youth has been mixed.

Minority youth may experience more individual ACEs (Cronholm et al., 2015; Duke et al., 2010;

Slopen et al., 2016), while White youth have higher ACE composite scores (Baglivio & Epps,

2016; DeLisi et al., 2017; Fagan & Novak, 2018; Perez, Jennings, Piquero, & Baglivio, 2016).

The effect of race/ethnicity may also depend on specific ACE indicators assessed (Hunt, Slack,

& Berger, 2017; Schilling et al., 2007; Slopen et al., 2016). For instance, Hunt et al. (2017) found that Black youth experienced more abuse and had more household members incarcerated while White youth had higher rates of household substance abuse.

The effect of race/ethnicity also varies depending on the outcome examined. For example, Schilling et al. (2007) discovered that the effect of ACEs on delinquency, drug use, and depression were higher for Whites at ages 18 to 20 compared to Blacks. In contrast, Fagan and

Novak (2018) examined a sample of 620 children from the Longitudinal Studies of Child Abuse and Neglect who were at a high-risk for maltreatment. The authors found that ACE composite scores were related to several forms of delinquency (arrest, alcohol use, and marijuana use) for

Black, but not White, youth. Fagan and Novak (2018) cautioned that the results were limited because they did not measure whether the outcomes were a one-time incident or more frequent.

Furthermore, DeLisi and colleagues (2017) were unable to find any pattern in the association between ACEs and offense type by race/ethnicity in a sample of 2,520 incarcerated male youth.

These disparate results may be attributed to a variety of explanations. Returning to general strain theory, Agnew (1992) argues that cumulative stress results in more negative outcomes. Since

Black youth experience more disadvantage (economic, social [racism]) and maltreatment in the

U.S., they may be less able to cope and have worse outcomes (Cronholm et al., 2015).

In contrast, Hunt et al. (2017) studied 3,043 children to examine whether ACEs were related to behavioral problems and found that children of a racial/ethnic minority were more likely to be exposed to adversity; however, White children were at a greater risk for behavioral problems (e.g., INT and EXT outcomes). The authors described how relative deprivation could be used to explain differences in outcomes for children of different race/ethnicities following

ACE exposure, where Black youth may be less susceptible to harmful effects because they generally experience more adversity compared to Whites, so additional adversity does not have as large of an impact. It is important to note that maltreatment, as measured by Hunt and colleagues (2017) was limited to incidents perpetrated by a child’s mother rather than any adult, which may have resulted in an underestimation of ACE exposure. Regardless, although the effects of ACEs on different racial/ethnic groups appears to be unclear, race/ethnicity should be considered in ACEs research due to potential experiences and effects of ACEs on outcomes.

Consequences of ACEs

Trauma can accumulate and strain neurodevelopment (Anda et al., 2006; Anda et al.,

2010), and early exposure to adversity has been associated with developmental issues during adolescence and adulthood (Cicchetti & Toth, 1995; Lamphear, 1985; Trickett & McBride-

Change, 1995). Researchers have also documented a relationship between ACEs and negative behavioral, mental, and physical outcomes (Felitti et al., 1998; Sacks et al., 2014). Maltreated

children are also more likely to have developmental difficulties regarding peer relationships, emotional regulation, schooling, self-concept, and psychopathology (Cicchetti & Toth, 1995).

Additionally, mental and physical exhaustion may result from exposure to trauma, resulting in debilitated information processing, decreased empathy, and antisocial behavior

(Ford, Chapman, Mack, & Pearson, 2006). Both direct and indirect trauma exposure have been associated with emotional and psychological distress compared to people who experience no victimization and others who are exposed to a single type of victimization (Espelage, Hong, &

Mebane, 2016; Turner, Finkelhor, & Ormrod, 2006). For instance, exposure to multiple types of trauma, or poly-victimization, was related to more trauma symptoms, including PTSD, depressive symptoms, anger, and anxiety (Finkelhor et al., 2007).

However, children who have a cumulative score of four or more ACEs have greater odds of experiencing several negative outcomes as adults (Felitti et al., 1998). Compared to children who have no exposure, children with four or more reported ACEs are 12 times more likely to have poor outcomes. Table 1 presents several negative consequences associated with ACEs, including medical complications, maladaptive coping, psychological and behavioral concerns, cognitive problems, continued victimization, and incarceration.

Table 1. Outcomes Related to ACEs in Adolescence and Adulthood Associated Outcome Citation Medical Disrupted neurological development Anda et al., 2010; Shonkoff et al., 2012; Teicher et al., 2003 Chromosomal damage Shalev et al., 2013 Insomnia Anda et al., 2010; Bellis, Lowey, Leckenby, Hughes, & Harrison, 2014; Chapman, Dube, & Anda, 2007 Cancer; lung, heart & liver disease; skeletal Bellis et al., 2014 fractures Maladaptive health habits Shonkoff et al. 2012 Heavy drinking, smoking, morbid obesity Bellis et al., 2014 Psychological Green et al., 2010; Kessler et al., 1997; McLaughlin et al., 2012 INT problems Hoeve et al., 2015; Rytilä-Manninen et al., 2014; Schilling et al., 2007 Depression Afifi, Brownridge, Cox, & Sareen, 2006; Anda et al., 2010; Bellis et al., 2014; Chapman et al., 2007; Frounfelker, Klodnick, Mueser, & Todd, 2013 Anxiety Anda et al., 2010; Bellis et al., 2014; Chapman et al., 2007; Turner et al., 2006 PTSD Anda et al., 2010; Bellis et al., 2014; Chapman et al., 2007; Frounfelker et al., 2013 Eating disorders Anda et al., 2006, 2010; Bellis et al., 2014; Chapman et al., 2007 EXT problems Afifi et al., 2006; Hoeve et al., 2015; Rytilä-Manninen et al., 2014; Wilson, Stover, & Berkowitz, 2009 Personality disorders Afifi et al., 2010 ASPD Douglas et al., 2011 Behavioral disorders McLaughlin et al., 2012 CD, ODD, ADHD Anda et al., 2006, 2010; Bellis et al., 2014; Chapman et al., 2007 Alcohol/drug abuse Afifi et al., 2006; Anda et al., 2010; Bellis et al., 2014; Chapman et al., 2007; Dube et al., 2003; Evans-Chase, 2014; Mersky et al., 2013; Perez et al., 2016; Perez, Jennings, & Baglivio, 2018; Vaughn et al., 2017; Young, Hansen, Gibson, & Ryan, 2006 Suicidality Dube et al., 2001; Duke et al., 2010; Evans-Chase, 2014; Frounfelker et al., 2013; Perez et al., 2016; Turner et al., 2006 Self-mutilation/harm Duke et al., 2010; Evans-Chase, 2014; Frounfelker et al., 2013 Behavioral Aggression Anda et al., 2006; Chapman et al., 2004; Perez et al., 2016; Turner et al., 2006 Impulsivity Ford et al., 2006; Perez et al., 2016; Roy 2005 Violence Bellis et al., 2014; Dembo et al. 1995; Dube et al., 2003; Duke et al., 2010; Evans-Chase, 2014; Felitti et al., 1998; Maxfield & Widom, 1996; Teague et al., 2008 Bullying, physical fighting, dating violence Duke et al., 2010 Risky sexual behavior Greene, Ennett, & Ringwalt, 1999; Kaestle, 2012; Reid, 2014; Roe-Sepowitz, 2012 Delinquency and/or criminal activity Baglivio & Epps, 2016; Baglivio et al., 2014, 2015; Barret et al., 2014a; Dembo et al. 1995; Dube et al., 2006; Duke et al., 2010; Evans-Chase, 2014; Fox, Perez, Cass, Baglivio, & Epps, 2015; Maxfield & Widom, 1996; Rivera & Widom, 1990; Teague et al., 2008; Vaughn et al., 2017; Wolff, Baglivio, & Piquero, 2017 Sexual offending Abbiati et al., 2014; DeLisi, Kosloski, Vaughn, Caudill, & Trulson, 2014; Drury et al., 2017; Greene et al., 1999; Kaestle, 2012; Levenson, Willis, & Prescott, 2016; McCuish, Cale, & Corrado, 2017; Reid, 2014; Roe-Sepowitz, 2012 Difficulty with rehabilitation Baglivio et al., 2014 School difficulties Bellis et al., 2014; Perez et al., 2016; Shonk & Cicchetti, 2001 Revictimization Greene et al., 1999; Kaestle, 2012; Reid, 2014; Roe-Sepowitz, 2012 School bully victim Logan, Leeb, & Barker, 2009; Widom, Czaja, & Dutton, 2008 Incarceration Bellis et al., 2014; Dembo et al. 1995; Maxfield & Widom, 1996 Recidivism Baglivio et al., 2014, 2015; Barrett et al., 2014a, 2014b; Dembo et al., 1993, 1995; Maxfield & Widom, 1996; Teague et al., 2008; Wolff & Baglivio, 2017

Yet, the processes that drive long-term consequences are not clearly understood.

Outcomes of ACEs may derive from solutions used to cope with trauma when other, healthier avenues are unavailable (Larkin, Felitti, & Anda, 2014). These strategies impact neurological functioning and alter chemical makeup; in turn, negative behavioral and physiological reactions can result in health, behavioral, and psychological concerns (Garland, Boettiger, & Howard,

2011). Certain youth may be especially vulnerable to negative outcomes due to genetics, as they may be predisposed to atypical behavior or MHPs. When they experience maltreatment, biological predispositions interact with those experiences and contribute to poor outcomes (Caspi et al. 2002; Caspi & Moffitt, 2006). Moreover, some children who experience maltreatment develop responses that protect them physical and emotionally in the moment; however, as they age, these survival responses become maladaptive and hinder psychosocial development

(Teicher et al., 2003). Self-regulation in behavior and emotional responses are also affected

(Evans-Chase, 2014). In short, trauma during childhood can result in long-term changes in cognitive and biological functioning (Lanius, Vermetten, & Pain, 2011; Mills et al., 2010).

Moreover, experiences of stress in childhood can impair coping as the child experiences cumulative stress in such a way that, as the child ages, he or she is vulnerable to stressful events

(Turner & Butler, 2003; Turner & Schieman, 2008). Accumulated stress creates a burden that can result in mental health disturbances (Aneshensel, 2009; Springer, 2009). That is, ACEs early in life prime a person to experience further stress and/or trauma, which can result in MHPs because they are not able to tolerate stress later in life (Hammen, Henry, & Daley, 2000;

Harkness, Bruce, & Lumley, 2006).

However, not all changes are biological or neurological. Social support also influences outcomes associated with ACEs, as social relationships are important for adolescents,

particularly the relationships they have with family and friends (Higgins & McCabe, 2000).

Adolescents’ identity is shaped through interactions with friends and family (Noach, Kerr, &

Olah, 1999). Yet, if adolescents do not perceive support, especially during stressful times, then social isolation may result and contribute to inadequate psychological functioning (Thompson,

2015). For example, people with a history of abuse or neglect report less social support compared to people without a trauma history (Sperry & Widom, 2013). Furthermore, social and cognitive development may be affected, resulting in impeded social adjustment and difficulty having healthy interactions with others (Koizumi & Takagishi, 2014).

ACEs and victimization. Abuse during childhood has been associated with risk for victimization, including intimate partner violence (IPV), during adulthood (Gover, Kaukinen, &

Fox, 2008). Additionally, other researchers have surmised that risk factors or vulnerabilities that put a person at risk for one form of violence may also increase the likelihood of revictimization for another type of violence (Murphy, Elklit, & Shevlin, 2017). The relationship between exposure to violence and deviant behavior has been termed the victim-offender overlap (Farrell and Zimmerman 2017). Offenders frequently report a history of victimization (Widom, 1989); however, this is not to say there is necessarily a causal relationship between victimization and offending. Rather, this relationship is dependent on victimization type, offending type, frequency of victimization, and the context within which an individual experiences violence (Farrell &

Zimmerman, 2018). Some victims or witnesses of violence may later engage in violent behavior because acceptance of violence as a mechanism to resolve conflict has been reinforced for them

(Jennings, Park, Tomsich, Gover, & Akers, 2011). In short, exposure to trauma may contribute to a cycle of violence experienced and perpetrated throughout the youth’s life.

ACEs and substance use. Individuals who report ACEs are more likely to use and/or abuse substances (Dube et al. 2003; Mersky et al., 2013; Perez et al., 2018; Vaughn et al., 2017;

Young et al., 2006). In a study investigating differences between youth with no substance use, those with use but no life disruption, and youths with use and life disruption, van der Put,

Creemers, and Hoeve (2014) found that youth with no use had the most protective factors. Also, youths with substance abuse problems had the highest recidivism rates, and the effect of risk and protective factors was strongest for abstainers, potentially indicating that programming intended to address risk and protective factors may not be as effective for youth with substance use issues.

Furthermore, Perez et al. (2018) reported that substance abuse had a partial mediation effect on the association between ACEs and serious, violent, and chronic deviant behavior. In short, ACEs appear to be related to substance use and offending; additionally, substance use and delinquency appear to be associated (Craig, Intravia, Wolff, & Baglivio, 2019). Dube et al.

(2006) found that increases in ACE scores were associated with an earlier onset of alcohol use.

Furthermore, Merrick, Ports, Ford, Gershoff, and Grogan-Kaylor (2017) assessed the effect of

ACEs on mental health and substance use. They discovered that higher ACE scores were related to a greater likelihood of substance use, depressed affect, and attempted suicide. Consequently,

ACEs may be strongly related to substance use. The consequences and negative coping strategies described by the above research may result in youth becoming involved in the juvenile justice system.

Justice-Involved Youth and ACEs

Maltreatment in childhood may increase the probability of youth delinquency and/or recidivism (Baglivio et al., 2014; Baglivio et al., 2015; Barrett et al., 2014; Maxfield & Widom,

1996; Teague et al., 2008). Compared to youth who are not involved in the juvenile justice

system, those who are report a higher rate of ACEs (Abram et al., 2004; Baglivio et al., 2014;

Dierkhising et al., 2013; Evans-Chase, 2014). Approximately 90% of youth offenders report experiencing trauma during childhood (Dierkhising et al., 2013, p. 6). Baglivio et al. (2014) found that about 95% of youth offenders in Florida had experienced one or more ACEs and approximately 50% reported four or more ACEs (p. 9). Youth classified as high-risk also reported a higher ACE composite score compared to youth from all other risk classes.

Furthermore, youth with trauma experiences start committing crimes earlier in life than people who do not report ACEs (Baglivio et al., 2015; Barret et al., 2014b; Rivera & Widom, 1990).

Additionally, youth with reported ACEs have more arrests and have a higher likelihood of incarceration as an adult (Maxfield & Widom, 1996; Mersky & Topitzes, 2010).

Among youth offenders, those who report more ACEs are more likely to be incarcerated

(Zettler, Wolff, Baglivio, Craig, & Epps, 2017), to recidivate (Craig, Baglivio, Wolff, Piquero, &

Epps, 2017; Wolff & Baglivio, 2017), and to reoffend more quickly (Wolff et al., 2017).

Consequently, ACEs have a considerable impact not only on justice-involved youth generally, but also onset and persistence of serious offending. One subset of offenders has even higher rates of reported traumas compared to offenders with less severe offenses: serious, violent, and chronic (SVC) offenders (Dierkhising et al., 2013; Fox et al., 2015; Fox, Piquero, & Jennings,

2014; Laub & Sampson, 1994; Piquero et al., 2003).

Fox and colleagues (2015) examined the utility of using ACEs to identify SVC youths.

They found that youth with a higher ACE score had a greater likelihood of being classified as an

SVC. Moreover, each additional ACE increased youths’ likelihood of committing serious, violent, and chronic offenses by 35%. These findings align with Moffit’s (1993) description of

LCP youth offenders. As such, ACEs are associated with chronic and serious offending. If ACEs

go unidentified, then a subset of vulnerable youth may go on to inflict a great amount of harm on others. However, if these youth are identified, such harm might be prevented by addressing these traumatic experiences, which results in reduced individual, social, and economic costs.

As the above research demonstrates, youth may have a risk or need in the form of ACEs before they have contact with the juvenile justice system. This is not an insignificant concern. In

2016, approximately 856,130 youth were arrested (OJJDP, 2017); therefore, many of those youth have likely experienced trauma and may go on to commit serious, violent crimes or suffer mental health issues if their ACEs go unnoticed. ACEs should be given consideration when deciding on disposition placement and programming for youth offenders. By identifying ACEs prior to youth entering the juvenile justice system (e.g., through screening in school or other settings), it may be possible to prevent the outcomes associated with ACEs. Other youth characteristics and/or circumstances may help lessen the effect of ACEs.

ACEs and Protective Factors

Some researchers have investigated the role of protective factors in decreasing recidivism likelihood and other outcomes (e.g., mental health) for youth who experience ACEs. For instance, Nurius, Logan-Greene, and Green (2012) discovered that presence of socioemotional support affected the relationship between ACEs and MHPs. In a separate study utilizing the same sample, Logan-Greene, Green, Nurius, and Longhi (2014) reported that overall life satisfaction and sleep quality moderated the association between ACEs and mental/physical health.

Additionally, Craig, Baglivio, Wolff, Piquero, and Epps (2017) investigated the effect of social bonds in buffering the effect of ACEs on reoffending and discovered that these bonds did not moderate the impact of ACEs on recidivism. In a different study, Craig et al. (2019) examined the effect of substance non-use as a protective factor in the relationship between ACEs

and recidivism. Youth with moderate-to-high substance use had a higher likelihood of reoffending if they also reported ACEs, but ACEs did not influence recidivism for youth with no reported substance abuse issues. These authors surmised that substance abuse is an important factor when examining the relationship between ACEs and delinquency. Although some factors may help decrease the impact of ACEs, the above findings demonstrate a relationship between

ACEs and MHPs, which may also affect youth offending.

Youth and Mental Health Issues

Nearly 20% of youth in the U.S. experience a mental disorder at some point in their lifetime to the extent that the disorder causes disfunction in their daily lives, while about half of youth could be clinically diagnosed with at least one mental health concern (Merikangas et al.,

2010, p. 987). For 80% of these youth, mental disorders start early in life: anxiety symptoms as young as 6, behavior disorders around age 11, mood disorders at age 13, and substance use disorders (SUDs) near 15 years of age (p. 984). Even when effective treatment is available, there are often delays between the first symptom and when people seek help. This lag in treatment is cause for concern, as mood disorders (e.g., depression, bipolar disorder, and dysthymia) constitute the most common cause of hospitalization for persons aged 10 to 17 (Agency for

Healthcare Research and Quality, 2009, p. 79). Consequently, if MHPs are not prevented or treated early in life, then negative consequences of them can span into adulthood.

Merikangas and colleagues (2010) examined the lifetime prevalence of mental disorders amongst youth. In their sample, 11.2% of youth reported a severe mood disorder (e.g., bipolar disorder or depression), 9.6% a severe behavior disorder (e.g., ADHD, ODD, CD), and 8.3% a severe anxiety disorder (p. 983). Approximately 40% of affected youth reported co-occurring disorders (p. 984). The number of youths with a mental disorder outpaced the most frequent

physical conditions adolescents experience, such as diabetes or asthma (Merikangas et al., 2010).

Accordingly, addressing youth MHPs is critical to help these youth thrive, as the U.S. is facing a public health crisis when it comes to youth with mental health concerns.

This crisis is only increasing for youth under 18 years, as mental health issues are indicated to be rising (Perou et al., 2013). Twenge et al. (2010) found that the prevalence of mental disorders, particularly anxiety and depression, in youth has risen over the last five decades. Between 2005 and 2011, 6.8% of children 3 to 17 years old were diagnosed with

ADHD, 3.5% had a conduct or behavioral problem, 3% had anxiety, and 2.1% were diagnosed with depression (Perou et al., 2013, p.1). For youth aged 12 to 17, 4.7% were diagnosed with a drug use disorder and 4.2% with an alcohol use disorder (p. 1). In 2010 alone, suicide was the second leading cause of death in youth aged 12 to 17, third for youth aged 10 to 14, and third for the 15 to 24 age group (CDC, n.d.). Furthermore, over 90% of youth who commit suicide have one or more MHPs (Shaffer & Craft, 1999).

Still, many youths fail to seek help for their MHPs due to not being aware of their health concerns and because of stigma surrounding mental health issues (U.S. Department of Health and Human Services, 1999). Youth with psychiatric problems are at risk for substance abuse and involvement with the juvenile justice system, resulting in high costs to themselves, their families, and the community. Furthermore, Bloom, Cafiero, and Jané-Llopis (2011) estimated the global costs of non-communicable diseases and projected these costs to 2030. They found that MHPs created the most costs, at $2.5 trillion globally in 2010 and $6 trillion by 2030 (p. 27). To provide some perspective, costs associated with mental disorders were greater than the combined costs of respiratory disorder, cancer, and diabetes. For the U.S. specifically, expenditures for mental health care in 2009 totaled $147 billion (Substance Abuse and Mental Health Services

Administration, 2013, p. 17). Accordingly, MHPs in youth are costly, both in terms of well-being and in economics. These costs are also relevant to the juvenile justice system.

Justice-Involved Youth and Mental Health Issues

Mental and substance abuse disorders are prevalent in the juvenile justice system (Teplin et al., 2002; Vermeiron, 2003; Wasserman et al., 2004). Approximately 20% of children in the community have a mental disorder (U.S. Public Health Service, 2000, p. 11). Conversely, Otto et al. (1992) reported that rates of mental health issues are substantially higher in justice-involved youth. For instance, about 65 to 70% of detained youth report a diagnosable mental health issue

(Teplin et al., 2002, p. 1136; Wasserman et al., 2004, p. 3; Wasserman, McReynolds, Lucas,

Fisher, & Santos, 2002, p. 317).

Moreover, these mental health needs are typically complex, with up to 27% of justice- involved youth reporting a serious (e.g., causing functional impairment) mental health issue

(Shufelt & Cocozza, 2006, p. 4). Many of these youth with MHPs experience two or more co- occurring disorders (79%), while 60% meet the criteria for three or more diagnoses (Shufelt &

Cocozza, 2006, p. 3). Additionally, of youth with a mental health disorder, 60.8% also meet the diagnostic criteria for a SUD (p. 3). Justice-involved youth with a mental health concern have a higher likelihood of reoffending compared to those without MHPs (Skeem, Winter, Kennealy,

Louden, & Tatar, 2013). As the above findings indicate, a substantial number of justice-involved youth experience MHPs, and many of these youth are sent to correctional facilities, rather than community-based treatment and/or may go on to recidivate.

This high prevalence of mental health issues in youth correctional facilities is not necessarily due to MHPs being related to offending; rather, detention facilities may be used as a place to hold youth with mental health concerns who otherwise cannot receive treatment in the

community (Government Accounting Office, 2003; Skowyra & Cocozza, 2007). The inter- systemic perspective suggests that a lack of community mental health services in the 1990s

(Grisso, 2008) resulted in communities utilizing the juvenile justice system to provide services to youth with MHPs. The juvenile justice system became a de facto mental health treatment provider and/or became an option to detain youth with serious disturbances.

Yet, the juvenile justice system is not the most effective option for providing treatment, as the relation between the state and youth is adversarial. Effective therapies involve trust and care, which can be difficult to achieve in juvenile justice settings. Furthermore, once youth have a history of juvenile justice system contact, community mental health providers may perceive them as dangerous and are not willing to provide services to these youth, resulting in a greater likelihood that these youth with MHPs will not receive services while in the community

(Cocozza, Skowyra, & Shufelt, 2010).

Mental health issues and substance use. Substance abuse is of particular concern for the juvenile justice system, as approximately 25 to 50% of detained youth report a history of substance use and/or abuse (Abrantes et al., 2005; Dembo, 1996). Odds of recidivism may be up to two times higher for individuals who use substances compared to those who do not (Bennett,

Holloway, & Farrington, 2008). Additionally, serious and chronic offenders are more likely to use substances and/or have SUDs (Mulvey, Schubert, & Chassin, 2010).

Youth who have co-occurring MHPs and SUDs represent the intersection between the mental health and juvenile justice systems as they have a mental health issue but are also engaging in an illegal behavior. However, the two systems treat substance use differentially.

Whereas the mental health system views substance use as a disease that may be treated, the

justice system perceives use as an illegal act with potential harm to the community that must be stopped (Shelton, 2002).

Past researchers have demonstrated that co-occurring mental health and substance abuse issues increase the seriousness and number of offenses in youth offenders (Hoeve, McReynolds,

Wasserman, & McMillan, 2013). These authors also found that youths with only SUDs had a higher risk of escalation of delinquency severity and frequency. In other words, substance use may be a stronger predictor of reoffending than MHPs, which aligns with other research (Bonta

& Andrews, 2017; El Sayed et al., 2016). Yet, trauma may be of even greater concern to the juvenile justice system, particularly if youth are using substances to cope with ACEs.

Mental health issues, trauma and the juvenile justice system. Childhood maltreatment is prevalent among justice-involved youth and has a strong relationship with MHPs, particularly if a youth has experienced sexual abuse (King et al., 2011). Both mental health issues and trauma increase the risk for contact with the juvenile justice system (Dembo, 1996; Grisso, 1999;

Vermeiron, 2003; Wasserman et al., 2004). Despite the association between mental health and trauma, little research has been conducted to more thoroughly examine this relationship, and empirical work on this association could better inform treatment for youth who have experienced trauma and MHPs. This gap in the research has potentially negative consequences for youth offenders with a trauma and mental health history.

Additionally, time spent in the system can exacerbate preexisting mental health issues, particularly if youth are placed in correctional facilities, which can be a traumatic experience

(Kupers, 1999). The proportion of incarcerated youth with MHPs may be cause for concern because they will return to the community and may have been traumatized while in the juvenile

justice system. Trauma has also been associated with different types of mental health disorders

(King et al., 2011), again emphasizing the importance of examining different types of MHPs.

INT and EXT Disorders

Certain behavioral disorders, such as CD, ODD, and SUD, are disproportionately prevalent in detained youth (Aarons, Brown, Hough, Garland, & Wood, 2001; Teplin et al.,

2002), and it is not uncommon for EXT behaviors to continue into adulthood. For instance, many youths with early-onset CD go on to be diagnosed with ASPD as adults (Beauchaine et al., 2017;

Robins, 1966). Approximately three to six percent of males and one percent of females are diagnosed with CD or ASPD (APA, 2013), but approximately half of violent and property offenses are committed by people with these diagnoses (Farrington et al., 1986); thus, nearly half of incarcerated men in the US meet the diagnostic criteria for ASPD (Beauchaine et al., 2009), and it can be costly. Inmates who qualify for ASPD account for more than 70 billion dollars in correctional costs (Schmitt, Warner, & Gupta, 2010; Teplin, 1994). As such, the prevalence and symptoms of ASPD, as well as the costs associated with inmates who have ASPD, present as a substantial concern to the correctional system. Yet, co-occurring disorders can have an even larger impact on reoffending. For instance, comorbid ADHD and CD increases the probability of chronic offending in adolescence (Barkley, 1996). Therefore, identification of specific types of

MHPs can better inform RNAs and programming.

Behavioral disorders have been generally classified into either INT and EXT symptoms

(Cicchetti & Toth, 1991). In a factor analysis of adult psychopathology, Krueger (1999) found a consistent latent structure of two higher-order factors: INT and EXT syndromes. The latent INT factor included phobias, panic disorder, generalized anxiety, PTSD, dysthymia, and major depression. Broadly speaking, INT disorders are characterized by emotions being directed

inward, resulting in anxiety, depression, and/or somatic complaints (Achenbach & Edelbrock,

1978, 1981; Zigler & Glick, 1986).

Antisocial personality disorder (ASPD), as well as drug and alcohol dependence, were found in the EXT factor (Kupers, 1999). Research has extended the EXT factor to include

ADHD, ODD, and CD (Lahey, Van Hulle, Singh, Waldman, & Rathouz, 2011; Tuvblad, Zheng,

Raine, & Baker, 2009). Youth with EXT disorders tend to direct their emotions towards other people or things (Hoeve et al., 2015). ADHD is represented by attention problems, restlessness, and impulsivity, while ODD is manifested by defiant, angry, irritable, and argumentative behavior. CD is characterized by antisocial behavior and involves a pattern of behavior that is repetitive, persistent, and violates the rights of others (American Psychiatric Association, 2013).

Most of the behavioral disorder research, as it pertains to crime, has focused on EXT disorders (Achenbach, 1985; Achenbach & Edelbrock, 1981; Robins, 1966). Part of this discrepancy is the result EXT symptoms being more observable than INT disorders (Links,

Boyle, & Offord, 1988). However, past research has identified an association between depression and violent/aggressive behavior (Fazel et al., 2015; Okzan, Rocque, & Posick, 2018), particularly for females (Posick, Farrell, & Swatt, 2013). Okzan et al. (2018) investigated the temporal ordering of depression and offending in a sample of serious youth offenders (N = 1,354) and found that, longitudinally, depression was a risk for income-related and aggressive offenses.

Despite manifesting in different behaviors, INT and EXT disorders may have more in common than they do not in terms of their potential causes and outcomes.

Etiology and outcomes. Transactional models of antisocial behavior have become popular (Beauchaine et al., 2009; Dishion & Racer, 2013) as a result of greater understanding regarding interactions between genetics, neural functioning, and environmental stressors (e.g.,

child maltreatment; Beauchaine & Gatzke-Kopp, 2012; Cicchetti, 2008). The same is true of INT and EXT disorders; both have been shown to be highly heritable but the specific disorder that results is subject to environmental shaping (Beauchaine, 2015; Dhamija, Tuvblad, & Baker,

2016; Lahey et al., 2011; Tuvblad et al., 2009). There is evidence that INT and EXT syndromes have a common etiology, including similar genetic influences (Cosgrove et al., 2011; Tackett et al., 2013) and central nervous functioning associated with negative affect (Beauchaine &

Constantino, 2017; Zisner & Beauchaine, 2016).

Statistical analyses of MHPs almost always result in an INT factor that accounts for a substantial amount of covariation among anxiety, withdrawal, and depression syndromes (Lahey et al., 2012). An EXT factor accounting for covariation among impulsivity, aggression, and delinquency is also typically found in such analyses. Additionally, statistical analyses have yielded a higher-order factor that accounts for some of the covariation between INT and EXT disorders. The dual factor structure is consistent across samples of children, adolescents, and adults; female, male, and mixed-gender samples; and self-reports, parent-reports, and teacher- reports (Achenbach & Edelbrock, 1978, 1981; Caspi et al., 2014; Lahey et al., 2011; Tackett et al., 2013). INT (Pianta & Castaldi, 1989) and EXT (Loeber, 1982; Robins, 1966) behaviors also remain stable over time, and early experiences shape later manifestation of these disorders.

Brain development is impacted by genetic-environmental interactions. For instance, the prefrontal cortex is instrumental in mood and emotion regulation as we age (Heatherton, 2011), but development of the prefrontal cortex is impacted by genetic (Colantuoni et al., 2011; Lenroot et al., 2007) and environmental risk factors (Hanson et al., 2010), which can result in serious behavioral concerns (Mead, Beauchaine, & Shannon, 2010). Disrupted development of the prefrontal cortex can result in deficiencies in impulse control, emotion regulation, and executive

functions (Arnsten, 2009). Two traits have been frequently linked to the development of INT and

EXT behaviors, respectively: anxiety and impulsivity.

Trait anxiety in childhood can result in vulnerability to early-onset anxiety disorders or social withdrawal (Kagan, 2013; Tambs et al., 2009). Trait impulsivity can contribute to early- onset conduct issues and ADHD (Beauchaine & McNulty, 2013; Krueger et al., 2002), particularly if a child experiences adversity (Beauchaine, Gatzke-Kopp, & Mean, 2007;

Beauchaine et al., 2009; Crowell, Beauchaine, & Linehan, 2009). As an example, male youth who are impulsive but reared in a protective environment may only show symptoms of ADHD

(Beauchaine, Hinshaw, & Pang, 2010; Beauchaine & McNulty, 2013), while similar youth may develop severe EXT behaviors if they experience child maltreatment (Beauchaine, Crowell, &

Hsiao, 2015; Nugent, Tyrka, Carpenter, & Price, 2011) and/or neighborhoods characterized by criminality and violence (Meier, Slutske, Arndt, & Cadoret, 2008).

Furthermore, emotion dysregulation is common in developing INT and EXT disorders

(Beauchaine, 2001), but it is socialized rather than inherited (Goldsmith, Pollak, & Davidson,

2008). However, when trait anxiety or impulsivity is coupled with emotional dysregulation, there is a strong likelihood for psychopathology (Beauchaine, 2001; Beauchaine & Gatzke-Kopp,

2012). In sum, whether youth develop INT or EXT disorders is not completely dependent on their biology; instead, youth are couched within families and communities that can aggravate a predisposition for these disorders.

EXT behaviors, specifically, follow a developmental trajectory, where many people will present with ADHD in childhood, which changes into ODD, then CD, and finally ASPD and/or

SUDs (Beauchaine, Zisner, & Sauder, 2017; Loeber & Hay, 1997). Yet, only about half of children with ADHD go on to develop severe EXT symptoms (Campbell, Shaw, & Gilliom,

2000). This pathway is either facilitated or halted by an interaction between neurobiological vulnerabilities and a high-risk environment that alters the prefrontal cortex and development of self-regulation. In other words, the progression of ADHD to ODD to CD to ASPD is highly dependent on environmental risk, such as affiliation with antisocial peers, substance abuse, neighborhood violence, and maltreatment (Beauchaine & McNulty, 2013).

Youth diagnosed with CD may interpret others’ behavior as hostile, even when benign

(Dodge & Pettit, 2003), because it is a type of adaptive response derived from the environment the youth has been raised in, particularly if he or she has been abused or neglected (Pollak &

Sinha, 2002). These hostile attribution biases contribute to youth aggressive social behavior

(Dodge, 1980; Dodge, Pettit, McClaskey, & Brown, 1986) and are associated with CD symptoms

(Dodge, Price, Bachorowski, & Newman, 1990). Maltreatment during childhood interacts with neurobiological vulnerabilities to increase the likelihood of EXT behaviors (Beauchaine et al.,

2017). However, there are gender differences in the prevalence and prognosis of EXT disorders.

Co-occurring disorders. INT and EXT disorders can be highly comorbid (Achenbach &

Edelbrock, 1983; Wolff & Ollendick, 2006), despite these two syndromes manifesting differently. Heterotypic comorbidity, specifically, is present for some individuals, where their primary disorder may be EXT, but they also have symptoms of an INT disorder (Zisner &

Beauchaine, 2016). Consequently, the EXT disorder may be targeted for treatment while symptoms of an INT syndrome are left untreated.

Co-occurring INT and EXT symptoms may be the result of an overarching factor that contributes to both behavior types (Achenbach & Edelbrock, 1983). People with comorbid disorders have more severe long-term consequences than people who experience either INT or

EXT problems, including criminal activity in young adulthood (Copeland, Miller-Johnson,

Keeler, Angold, & Costello, 2007; Hoeve, McReynolds, & Wasserman, 2013; Hoeve et al.,

2015). For instance, in a sample of 1,420 youths aged 9 to 16, Copeland et al. (2007) found that children who were not diagnosed with co-occurring disorders were less likely to be arrested for severe or violent offenses in young adulthood compared to children who were diagnosed with either: anxiety and SUDs; anxiety and CD; depression and SUDs; or depression and CD. The authors concluded that co-occurring disorders are a strong predictor of risk for criminality.

However, Copeland and colleagues (2007) also discussed how their results may be limited in generalizability, as the samples was not representative of the U.S. sample (e.g., they oversampled for American Indian children, and no Asian Americans or Hispanic children were included).

Additionally, youth in the juvenile justice system have higher rates of INT and EXT symptoms compared to the general population (Teplin et al., 2002). Comorbidity is also more common in justice-involved youth (Wasserman, McReynolds, Schwalbe, Keating, & Jones,

2010). Hoeve et al. (2015) suggest two explanations for why youth with co-occurring INT and

EXT disorders have a greater likelihood of offending as adults: trauma and an earlier onset of deviance.

Youth with a history of trauma may experience a cycle of violence, where they eventually commit crime as a result of their own victimization (Widom, 1989; Widom &

Maxfield, 2001; Wilson et al., 2009). Further, early onset offending has been associated with long-lasting offending (Farrington, 2006; Moffitt, 1993; Sampson & Laub, 1990, 1993). To investigate how co-occurring disorders are associated with criminal activity, Hoeve et al. (2015) examined youth offenders who either had no disorders, had an INT disorder, had a disruptive

(e.g., EXT) disorder, or who experienced comorbidity. These authors discovered that comorbid youth had higher rates of exposure to trauma than youth who had a diagnosed INT or EXT

condition. Consequently, exposure to trauma in childhood increases the risk for INT and EXT disorders (Hoeve et al., 2015; Wilson et al., 2009). These findings demonstrate the link between

ACEs and MHPs, as well as a relationship between mental health concerns and youth offending.

ACEs and Mental Health Issues

Individuals with reported ACEs may be more likely to develop mental health concerns.

For instance, past research has demonstrated a relationship between ACEs and: Depression

(Schilling et al., 2007); prescribed psychotropic medications (Anda et al., 2007); anxiety, mood, personality, and substance use disorders (Vaughn et al., 2017). Past research has demonstrated that cumulative experiences of trauma may exacerbate psychological health (Shevlin, Houston,

Dorahy, & Adamson, 2008). As an example, Willie, Kershaw, and Sullivan (2018) examined the effect of IPV on mental and sexual health in a sample of 212 women. The authors utilized latent profile analysis and identified three profiles: Low ACEs, Moderate ACEs, and High ACEs.

Women’s ACEs profile predicted the severity of their victimization as well as their posttraumatic stress and depressive symptoms, where women in the High and Moderate ACEs groups reported more MHPs and psychological IPV victimization than women in the Low ACEs class.

Vranceanu, Hobfoll, and Johnson (2007) also discovered a positive relationship between the number of reported maltreatment incidents and severity of depression and PTSD symptoms.

Moreover, Elklit, Karstoft, Armour, Feddern, and Christoffersen (2013) found that adults who had experienced PTSD following abuse during childhood or other trauma were nearly twice as likely to engage in criminal behavior.

Researchers have also examined the relationship of maltreatment to INT and EXT disorders. Hunt et al. (2017) examined the relationship between ACEs and behavioral problems in children. They discovered that ACEs had a strong association with EXT behaviors. However,

children who reported three or more ACEs were significantly more likely to display INT or EXT behaviors necessitating clinical intervention compared to children with fewer than three ACEs.

Experiences of victimization and witnessing violence have also been associated with several INT and EXT outcomes (Buka, Stichick, Birdthistle, & Earls, 2001; Fagan et al., 2014). For example,

McCabe, Hough, Yeh, Lucchini, and Hazen (2005) documented a relationship between victimization and conduct disorder.

Furthermore, past research has demonstrated that certain ACEs, such as parental incarceration, are related to EXT behaviors (Kjellstrand, Reinke, & Eddy, 2018; Murray,

Farrington, & Sekol, 2012). These associations are particularly strong if a youth and his or her family are experiencing multiple vulnerabilities (e.g., poverty, MHPs, substance use). Such experiences may be associated with EXT symptoms because children exposed to adversity may have multiple risks over time that accumulate and contribute to problem behaviors, which is consistent with cumulative risk models and risk theories (Gutman, Sameroff, & Cole, 2003;

Rutter et al., 1997). Yet, exposure to adversity does not guarantee that a child will go on to offend or have other problem behaviors. On the contrary, the number, type, intensity, and combination of risk and protective factors a youth experiences influences his or her trajectory.

Assessments that thoroughly document these different factors can facilitate development of interventions that focus on a youth and his or her family’s strengths while also addressing challenges or problem behaviors within the family (Kjellstrand et al., 2018).

Trauma, mental health, and substance use. Douglas et al. (2011) examined the relationship between ACEs, substance use, and ASPD. They found that presence of ACEs increased an individual’s likelihood of receiving an ASPD diagnosis. Furthermore, subjects diagnosed with ASPD were more likely to have an earlier onset of substance use concerns and

demonstrated a higher prevalence of SUDs than subjects who were not diagnosed with ASPD.

Additionally, people who experience symptoms of depression or distress following a traumatic event may self-medicate with alcohol or drugs (Langdon et al., 2017). Conversely, individuals with a history of substance use who experience a traumatic event may be more likely to develop depressive symptoms following the trauma, as they have a lower tolerance for distress (Buckner,

Keough, & Schmidt, 2007). In other words, the relationship between MHPs (e.g., depression) and substance use following a traumatic event are not always clear. Mental health symptoms may result in self-medication, or substance use may precede both the traumatic event and MHPs.

In a recent study, Craig, Zettler, Wolff, and Baglivio (2018) assessed the mediating impact of substance use, mental health issues, and co-occurring substance use and MHPs on the relationship between ACEs and recidivism. They demonstrated that drug use (but not alcohol use), MHPs, and co-occurring problems were partial mediators in this association, wherein these mediators accounted for 22 to 30% of the effect of ACEs on youth reoffending (p. 13). Hence, mental health issues and substance use may exacerbate the effect of ACEs on reoffending.

Although much work has been done on identifying ACEs via risk/needs assessments, particularly with the PACT, less has been done to examine MHPs via such instruments.

Mental Health and Recidivism

Although the Central Eight risk factors were originally developed for offenders broadly, other researchers have shown that these predictors are also relevant for individuals with mental health issues (Rezansoff, Moniruzzaman, Gress, & Somers, 2013). Past research has demonstrated that MHPs that do not include substance use are not directly associated with recidivism for many offenders with a mental health diagnoses (Peterson, Skeem, Kennealy, Bray,

& Zvonkovic, 2014). However, different types of MHPs may disparately predict recidivism.

For instance, Olver and Kingston (2018) found that incarcerated men in a correctional mental health facility diagnosed with schizophrenia or psychosis, anxiety disorders, or mood disorders tended to have lower rates of reoffending. Conversely, people with SUD, personality disorders (PD), or co-occurring diagnoses had higher odds of recidivism. Findings for this latter set of diagnoses are not unexpected, as both substance use and antisocial personality (e.g., PD) are accounted for in the Central Eight, suggesting that they are directly related to risk for recidivism. Essentially, these findings suggest that exploration of different types of MHPs may be critical to better understanding the relationship between mental health concerns and reoffending. Regardless, the effect of MHPs (and ACEs) on recidivism may be reduced through prevention and programming efforts.

Prevention and Programming

As discussed above, the juvenile justice has oscillated between rehabilitation and punishment for youth offenders (Bernard, 1992; Empey, 1978; Rothman, 2012). This trend has been especially apparent in recent decades. In 1974, Martinson released a report showing that rehabilitation efforts had little to no impact on reoffending. His results were interpreted as

‘nothing works’. Wilks and Martinson (1976) concluded that the correctional system should rely on deterrence and punishment to reduce recidivism. States established statutes mandating more severe sanctions for offenders (Feld, 1993). Yet, deterrence and harsh punishment have not been effective in decreasing reoffending (Smith et al., 2002). Consequently, we are currently moving back towards a framework of prevention and rehabilitation. Still, research is lacking regarding what is effective for youth (Loeber & Farrington, 2001).

Prevention is most effective when implemented during childhood and before youth become involved in reinforcement systems that promote antisocial behavior, such as affiliation

with antisocial peers and the juvenile justice system, and prior to youth starting to use substances

(Webster-Stratton & Taylor, 2001). Early intervention may also address youth needs and prevent delinquency and crime (Loeber, Farrington, & Petechuk, 2003). The juvenile justice system may reinforce delinquent behaviors, particularly in youth with CD and other EXT behaviors (Dishion

& Racer, 2013). Therefore, preventative efforts are necessary before youth contact the juvenile justice system; yet, this may not always be possible. Accordingly, programming can be used to address youths’ risks and needs.

Programming Based on Needs

One criticism of using risk instruments is that they are used to predict risk of reoffending rather than target an offender’s needs and then offering appropriate programming (Moore &

Padavic, 2011). Solely focusing on risk can be detrimental, as risk assessments are not free of error (Baglivio, 2009). Instead, programming should be based on need, as services can be harmful to some youth, especially if they are classified as low-risk. For instance, Gatti et al.

(2009) found that low-risk youth who were exposed to deviant peers had increased reoffending.

Additionally, many assessments utilize and weight criminal history items more than other items (Andrews & Bonta, 2010), and these items may overshadow other needs items, such as mental health or substance abuse concerns. Hence, if a youth offender does not have a criminal record, he or she may be classified as low-risk despite having mental health or substance abuse issues that may have contributed to the his or her offending. Consequently, that youth might not receive programming that he, or she, needs. Also, some static risk factors included in risk instruments, such as a history of abuse or trauma, may increase a youth’s risk score, thus punishing them for being a victim (Holsinger, 2014). Such risk items also indicate a potential need that could be addressed by programming but might not be if youth are seen as ‘risks’

instead of people in need of help. Consequently, it is essential to also address youths’ needs

(Baglivio et al., 2014). Moreover, a mismatch between youths’ risks/needs and services may result in more restrictive placements, which are expensive and may result in higher recidivism rates (Ryan, Abrams, & Huang, 2014).

States spend approximately 5.7 billion dollars each year to house youth offenders placed in residential facilities (Peeteruti, Walsh, & Velazquez, 2009). Yet, many youths in these facilities are not rehabilitated and may leave with more criminal tendencies (DeLisi, Hochstetler,

Jones-Johnson, Caudill, & Marquart, 2011), as secure facilities may have a deviant culture created by other violent youth. Youth may become traumatized, have their psychological maturation stalled, and/or have more delinquent behavior once released.

As such, a youth’s experiences in correctional facilities may be discordant with a rehabilitative approach (NeMoyer, Brooks, Goldstein, & McKitten, 2015). Moreover, many youths’ mental health and/or substance abuse problems persist while they are incarcerated

(Ramchand, Morral, & Becker, 2009). In short, youth correctional facilities are not optimal for providing youth who have trauma experiences and/or MHPs with programming.

ACES, prevention and programming. Youth who enter the juvenile justice system likely already have a history of ACEs. If we can prevent youth crime by addressing ACEs, then we might be able to reduce the amount of crime, particularly serious and violent crime (Fox et al., 2015), perpetrated by youth. Additionally, since youth offenders with an experience of maltreatment are more difficult to rehabilitate (Baglivio et al., 2014), prevention might be the most effective avenue to address these youths’ risks and needs. Consequently, a proactive approach, instead of the traditional reactive approach, is required. We can save approximately

$3.2 to $5.8 million by targeting first-time youth offenders and $2.6 to $4.4 million by targeting

high-risk youth (Cohen & Piquero, 2009, p. 40). Moreover, $5.7 million can be saved via effective, early interventions for high-risk youth. Consequently, the potential costs saved by addressing ACEs are substantial.

Preventative care for youth with ACEs can also reduce their need for medical, mental health, and/or substance abuse treatment in the future (Baglivio et al., 2014). As people with

ACEs frequently enlist antisocial behaviors or maladaptive coping styles to deal with their stress, incarceration does not help to diminish those behaviors. Stated otherwise, recognition of ACEs in youth offenders is essential for determining their dispositional placements, so as not to aggravate their antisocial behavior or worsen their accumulated stress. Furthermore, effective interventions can help halt the intergenerational risks associated with ACEs (e.g., the cycle of violence; Widom & Maxfield, 2001; Wilson et al., 2009), thus resulting in additional costs savings (Baglivio et al., 2014).

Kowalski (2018) investigated the interaction between programming (family-based treatments and ART) and ACEs on youth recidivism for males and females in Washington State.

Although family treatment and ART were associated with reduced odds of recidivism for males, only ART was related to decreased recidivism odds for females. Moreover, the interaction between programming and ACE summary scores was nonsignificant for both males and females, suggesting that these interventions are not particularly beneficial for youth who have been exposed to adversity. However, one avenue of secondary prevention is Trauma-Informed Care

(TIC), where the focus is shifted to asking a person what has happened to him or her instead of implying that the person is at fault for what has happened. Staff who work with youth could be trained in TIC so they can learn about trauma and posttraumatic reactions (Griffin, Germain, &

Wilkerson, 2012) and so they can refer youth to mental health professionals as needed

(Dierkhising et al., 2013). More importantly, youth could be screened for trauma upon entering the juvenile justice system (Baglivio et al., 2014).

Trauma screening is critical, as traumatized youth placed outside of their homes may become further traumatized because of familial separation, interactions with aggressive peers, and perceived feelings of a lack of safety. Girls, especially, may be vulnerable to traumatic stress responses as a result of their higher rates of traumatic experiences (Hennessey, Ford, Mahoney,

Ko, & Siegfried, 2004). In short, since many youths have reported ACEs, that information can not only be used to predict high-risk behavior, but also to provide them with interventions that address their trauma and potentially prevent recidivism (Baglivio et al., 2014).

Different Programming for Youth

Several programs exist for youth. Some are family-based while others focus on specific behaviors or skills, such as anger or social interactions. Mental health programming is also offered to some youth in the juvenile justice system. However, little research details the types of services that youth receive or characteristics (e.g., gender, age or race) of those who receive treatment (Shelton, 2005). This subsection reviews three3 family-based programs (Multi-

Systemic Therapy [MST], Family Integrated Transitions [FIT], and Functional Family Therapy

[FFT]), Aggression Replacement Training (ART), mental health treatment, consequences of ineffective treatment, and economical returns from efficacious programming.

Family-based treatments. Effective family-based programs start with the understanding that a youth’s behavior occurs within a social context that the youth interacts with and which shapes his or her behavior. Consequently, to change delinquent behavior, programs must target

3 Only three family-based therapies are discussed here, as Washington State collects programming data on MST, FFT, and FIT and not other types of family therapies (e.g., group therapy provided by a private practitioner).

interacting systems. Furthermore, MST and FFT are based in social learning theory (Bandura,

1977), where behavior is learned through imitation and reinforced in social interactions. As such, to be effective, the program must target interactions between youth and adults to improve the youth’s social adaptation. Stated otherwise, the reciprocal interactions between youth and adults are altered rather than just the youth’s behavior.

MST and FFT further draw from family systems theory (von Bertalanffy, 1969).

Accordingly, the family hierarchical structure, communication patterns, and familial roles are addressed in MST and FFT (Caldwell & Van Rybroek, 2013). Both programs also involve a focus on behavioral functioning, multi-dimensional treatment (e.g., variety of services individualized to the youth and his/her family’s needs), and continuous outcome monitoring.

More specifically, MST is a community-based treatment that emphasizes individualized plans and ascribes to youths’ weaknesses and strengths (Henggeler, Melton, & Smith, 1992).

Youths’ behavior is seen as the result of the youth’s interactions with different system (e.g., community, school, peers, and family; Landsman, 2013). MST enlists family counseling and assesses behavioral and psychological problems of youth nested within different systems, such as family, school, and community domains (Borduin et al., 1995). In the MST framework, systemic and cognitive factors associated with antisocial behavior are targeted (Schaeffer & Bourduin,

2005). Therapists work closely with families to examine and restructure youths’ environment across systems to decrease antisocial behavior (Timmons-Mitchell, Bender, Kishna, & Mitchell,

2006). Controlled studies have shown that treated youth are significantly less likely to reoffend

(Borduin et al., 1995; Borduin, Schaeffer, & Heiblum, 2009). MST also reduces delinquent behavior, substance use, family dysfunction, and psychopathology; however, it is more efficacious for younger youth (van der Stouwe, Asscher, Stams, Deković, & van der Laan,

2014). Moreover, compared to individual therapy, MST results in a greater reduction in recidivism (Schaeffer & Bourduin, 2005).

FIT is an offshoot of MST; it is also an intensive, family-based program, and it has been implemented in Washington State (Aos, 2004). FIT focuses on youth transitioning from corrections or a residential placement back to the home, and these youth typically have co- occurring SUDs and mental health disorders (National Registry of Evidence-based Programs and

Practices, 2017). Therapists work with youths and their families to reduce the youth’s likelihood of reoffending by connecting families with community supports and increasing youths’ prosocial behaviors. Youths’ substance use and mental health needs are also addressed. FIT draws from

MST, motivational interviewing, relapse prevention, and dialectical behavior therapy. In 2004,

Aos found that FIT reduced felony recidivism for treated youth.

Lastly, FFT is family-based but differs from MST and FIT as it focuses almost exclusively on the family system (Celinska, Furrer, & Cheng, 2013). FFT targets familial communication patterns that maintain problem behaviors. Reciprocal interactions are examined to improve communication patterns amongst family members (Alexander & Parsons, 1973). FFT also emphasizes how other systems, like the juvenile justice system, interact with the family.

Research demonstrates that FFT effectively improves family interactions and functioning, as well as communication between family members (Alexander, Pugh, Parsons, & Sexton, 2000).

Alexander and Parsons (1973) found that FFT contributed to a decrease in recidivism for treated families (26%) compared to non-treated families (57%). Other researchers have found that FFT is effective in reducing reoffending, drug abuse, and risk-taking (Friedman, 1989; Sexton &

Alexander, 2006; Sexton & Turner, 2010). As compared to individual therapy, FFT reduces recidivism by 20 to 60% (WSIPP, 2002, p. 3; Sexton & Turner, 2010, p. 345). Yet, Barnoski

(2004b) found that FFT did not impact recidivism. Still, he discovered that when the treated group was split into youth who had competent therapists and those with less competent therapists, reoffending was lower for the former and high in the latter. Moreover, Sexton and

Turner (2010) revealed that FFT is only successful in decreasing recidivism when treatment providers adhere to a treatment model.

Aggression Replacement Training. In 1998, Goldstein, Glick, and Gibbs created ART for youth. ART involves group training, where participates are taught social skills, moral reasoning, and anger management (van der Put et al., 2012). Barnoski (2004b) has found that

ART improved the behavior of delinquent or aggressive youth, as long as the program is correctly implemented. Therapist competence is also essential; as youth who received programming from less competent therapists had increased reoffending, while youth with competent therapists displayed decreased recidivism rates.

Mental health programming. Youth with mental health issues who do not receive needed treatment may continue to engage in violent and criminal behavior (Burns et al., 2003).

As an example, past researchers have found that disruptive behavioral problems are frequently not matched with appropriate services (Luong & Wormith, 2011; Peterson-Badali et al., 2015) despite these issues having a strong relationship with recidivism (Perrault, Vincent, & Guy,

2017; Peterson-Badali et al., 2015). Moreover, since mental health treatment on its own cannot reduce reoffending (Rice & Harris, 1997), programming for offenders with MHPs necessitates treatment addressing psychiatric symptoms and criminal behavior. However, youth who receive out-of-home mental health services may experience a delay in social and independent living skills while institutionalized, potentially impacting their development and later functioning in adulthood (Pullmann, 2011). Such out-of-home treatment options may only temporarily prevent

further criminal behavior because they are effectively incapacitated. As Pullmann found, institutionalization did not impact youths’ reoffending once they were discharged. Consequently, many youths with mental health concerns may return to their communities without their MHPs being addressed. In fact, youth with MHPs who are diverted from confinement, and who receive community-based treatment, have less reoffending than similar youth not provided with treatment (Cuellar, McReynolds, & Wasserman, 2006).

Despite the negative consequences of treatment while institutionalized, incarceration may be the first opportunity many youths with mental health and/or substance abuse concerns have to receive treatment (Rogers et al., 2006). Yet, the juvenile justice system may not offer much in the way of mental health or substance abuse care because it is not equipped to deliver such services (Farmer et al., 2003). Approximately 15 to 30% of detained youth with a mental disorder receive treatment (Shelton, 2005, p. 107; Teplin et al., 2005, p. 1775). Stated otherwise, most justice-involved youth with MHPs do not receive treatment.

There has been concern regarding the lack of treatment justice-involved youth with mental health needs receive (Burns et al., 2003). Rather than receiving treatment in the mental health care system or the juvenile justice system, many youth offenders with a mental health issue are shifted between systems, partly as a result of their complex emotional and behavioral problems, high costs of services, and uncertain effectiveness of treatment (Teplin, 1991).

Prioritization of mental health services differs depending on whether a youth is in the mental health or juvenile justice system. The former focuses on symptom reduction, quality of life, and decreased hospital readmissions (Test, 1992) while the latter emphasizes risk reduction

(Andrews & Bonta, 2010). In short, youth with a mental health need may not receive programming while in the juvenile justice system. Consequently, many youths who are released

face substantial mental health and/or substance abuse problems that go undetected or untreated while they are in the juvenile justice system (Skowyra & Cocozza, 2007). Yet, this concern starts well before a youth is placed in a residential or correctional environment. It is possible to divert youth to appropriate programming before they ever set foot in front of a judge.

Prior to a youth receiving a disposition, that youth’s mental status could be considered so judges have enough information about the youth’s mental health history and present need to make an informed decision (Skowyra & Cocozza, 2007). Without such information, as well as knowledge of community-based treatment options, judges may fall back to residential placement as a youth’s disposition if the youth has committed a serious crime. This decision point is critical, as youth can be diverted from the juvenile justice system and into community-based treatment if their individual situations warrant such action. Unfortunately, some youth do not receive an evaluation of their mental status until after they have been adjudicated.

Consequently, many youths placed in youth correctional facilities have mental health needs. According to Skowyra and Cocozza (2007), 76.4% of youth have one mental health diagnosis. However, compared to male youth (72.4%), female youth (87.2%) have higher rates of MHPs (p. 58). The most prevalent MHPs include disruptive disorders, SUDs, and anxiety disorders (Shufelt & Cocozza, 2006). Accordingly, mental health programming in youth correctional facilities is essential. However, even if programming is provided, concern remains about the quality of such programming.

Mental health services in the juvenile justice system generally do not align with evidence-based practices (Melton & Pagliocca, 1992). For secure facilities that offer mental health services, many youths do not actually receive them (NCMHJJ, 2005). Alcohol and drug abuse treatment, specifically, is not offered to youth in high rates. Approximately 46% (p. 3) of

youth in correctional facilities were diagnosed with a SUD, but most of these youth did not receive alcohol or drug treatment while incarcerated (Skowyra & Cocozza, 2007).

Therefore, mental health issues present as a potential risk and need for justice-involved youth. If youth do not receive treatment for their health concerns, they may go on to reoffend.

Effective mental health services are behavioral and/or cognitively oriented, highly structured, community-based, carried out with integrity, are more intensive, and target high-risk offenders

(Rice & Harris, 1997). In contrast, services based on deterrence or psychotherapy are less efficacious and may be harmful to offenders (Rice, Harris, & Cormier, 1992).

Programming Risk and Pay-Off

Many studies that have investigated treatment effectiveness have focused on youths’ demographics, such as gender or age (Kazdin, 2007) rather than characteristics associated to the problems being addressed by an intervention (e.g., ACEs, mental health). As discussed by van der Put et al. (2012), compliance with the Need principle requires a focus on risk factors that contribute to recidivism, rather than those associated with onset of deviant behavior as the former involves rehabilitative interventions while the latter focuses on prevention. Rehabilitative interventions may also necessitate consideration of age. As demonstrated by van der Put et al.

(2012), the relationship between dynamic risk factors and recidivism weakens across adolescence and the importance of different risk domains changes across time. For instance, the family domain demonstrated the strongest relationship with recidivism for younger justice- involved youth (age 12), relationships for 13-year-olds, and attitude for 14-year-olds.

Accordingly, programming may have to be altered for youth with continued contact with the juvenile justice system.

Moreover, certain treatment can result in adverse outcomes, so practitioners must exercise caution when deciding on an intervention. Again, the type of mental health issue is relevant, as some forms of psychological treatment can cause more harm than good depending on the diagnosis. For instance, many delinquency prevention programs focus on aggressive behavioral issues (Beyers & Loeber, 2003). Yet, other MHPs, such as depression, may present as a risk factor even if the individual does not exhibit violent behavior and should also be considered during programming (Okzan et al., 2018).

Additionally, youth who experience EXT symptoms and who receive group treatment may become more antisocial compared to similar youth with no treatment because their behavior is positively reinforced by antisocial peers (Dishion & Racer, 2013). Instead of focusing on just risk or needs, more attention needs to be given to youths’ responsivity to interventions.

Treatments based on risk, but that do not match youths’ needs, may result in harm to the individual and to the community. However, when programming is effective, it can be beneficial to youth and their communities.

Treating mental health issues is the most expensive medical expenditure ($13.9 billion) for youth wellness in the U.S. and is associated with more expenses than treatment for physical conditions (Soni, 2015). Mental health services may be underutilized as a result of cost, cultural barriers, access, a shortage of mental health professionals, and stigmatization of MHPs (Gabel,

2010). Yet, the cost of not addressing serious mental health needs outweighs the financial burden of treating youth with mental health issues. For example, a successful prevention program for a five-year-old with early-onset CD costs 6-12% of the 25-year expense of not providing intervention (Bonin, Stevens, Beecham, Byford, & Parsonage, 2011, p. 6).

Therefore, despite high initial investments, properly implemented programming for youth is prudent. Cohen (1998) estimated that over one million dollars can be saved if one high-risk youth desists from crime. Past research has shown that different treatments can be effective for youth offenders. Yet, high-risk youth offenders may be provided with fewer resources in practice due to the belief that nothing works for them (Ogloff & Lyon, 1998) despite the RNR framework suggesting that higher risk individuals require more treatment (Bonta & Andrews, 2017).

The treatments described above may also be economically sound. For instance, every dollar invested in MST results in savings of about 10 to 24 dollars, as lifelong criminality may be halted (Klietz, Borduin, & Schaeffer, 2010, p. 664). Furthermore, Aos (2004) conducted a cost- benefit analysis for FIT and found that the program produces a return of $3.15 for every dollar invested in treatment (p. 6). Additionally, Caldwell, Vitacco, and Van Rybroek (2006) utilized propensity scores to match 101 high-risk youths who had documented disruptive and aggression concerns and who received intensive treatment to 101 youth who received treatment at usual in a secure mental health correctional facility located in Wisconsin. The authors found that ART was cost effective for the treatment sample. These costs demonstrate the individual rewards

(desistance from crime) and societal benefits (decreased spending on youth offenders) resulting from effective programming.

Gender Responsivity

The above review details life course theory, the RNR framework, RNAs, ACEs, mental health issues, and programming. However, this broad discussion does not provide context for female-specific pathways to offending, which is a pertinent topic to the current study, as the proportion of female youth in the juvenile justice system has increased when compared to male youth (OJJDP, 2010; Schwalbe, 2008). A feminist pathways perspective (Daly, 1992) is

particularly useful in contextualizing the topics involved in the present study, as it involves gender-specific dynamics, wherein the effect of abuse during childhood, and its relationship to later mental health and substance use issues, is emphasized (Broidy, Payne, & Piquero, 2018). It is argued that these specific risks are an important predictor in females’ pathways to offending

(Chesney-Lind, Morash, & Stevens, 2008). Differences between males and females can be found within the RNR perspective, risk instruments, experiences of trauma, and prevalence of MHPs.

In turn, consideration of these gender differences may inform gender responsive programming.

Life Course Theory

As discussed previously, there is heterogeneity in offending trajectories, particularly for males (Moffitt, 1993; Piquero, Brame, & Moffitt, 2005), and much of the developmental and life-course research has focused on male offenders (McGee & Mazerolle, 2016). Although females also demonstrate differences in offending careers (Cauffman, Monahan, & Thomas,

2015), they tend to have an onset of deviant behavior in adulthood rather than adolescence

(Andersson, Levander, Svensson, & Levander, 2012) and are less likely to be chronic offenders

(D’Unger, Land, & McCall, 2002).

Furthermore, certain risk factors appear to be particularly salient in identifying persistent offending for females. Broidy et al. (2018) found that when females have an early onset and/or chronic offending, they typically report abuse in childhood, INT symptoms, and drug use. Stated otherwise, differing pathways to offending and resulting trajectories suggest a need for gender- specificity in identifying risk factors across genders. Additionally, Cauffman et al. (2015) found that MHPs, adversarial interpersonal relationships, and exposure to violence predicted chronic female offending, but these risks were not as pertinent in male trajectories. Differential risk exposure within female groups of offenders also includes gang and child welfare involvement,

conduct disorder, and temperament issues (Wolff, Baglivio, Vaughn, DeLisi, & Piquero, 2017).

Females who report these experiences are more likely to be serious, chronic offenders.

Moreover, El Sayed, Piquero, and TenEyck (2017) described how males and females exhibit similar risk factors associated with longitudinal pathways; yet gender differences in the exposure and effect of specific risk factors may help explain why the genders are identified in different trajectory groups (e.g., early vs. late onset, chronic offending vs. desistance; Gunnison,

2014). Hence, although certain risk factors have been identified for males and females, the RNR framework as a whole does not include a discussion of how gender-specific exposure and reactions to several risks affects recidivism likelihood.

RNR and Need-Service Matching

According to Vitopoulos et al. (2012), the RNR model may not generalize well to female youth, as needs specific to females have not been sufficiently addressed. The feminist pathways literature has identified a relationship between early abuse or other trauma that results in girls leaving their homes and ending up on the streets (Gilfus, 1993). Once homeless, these girls may cope with MHPs resulting from earlier adversity by using alcohol or drugs and may engage in prostitution to survive. These coping and survival strategies result in these girls becoming justice-involved, and the justice system may be unable to identify or provide services for the risks and needs these girls have (Chesney-Lind & Pasko, 2013). This particular pathway has been termed the ‘abuse to prison pipeline’ (Saar, Epstein, Rosenthal, & Vafa, 2015).

More recently, Broidy and colleagues (2018) studied a sample of 470 female offenders in

Australia and discovered a pathway between early abuse (e.g., physical, sexual and emotional),

INT symptoms, drug use, and offending for females with an early onset of deviant behavior.

Although abuse is not considered a criminogenic need, it appears to have contributed to a well-

established criminogenic factor – substance use – that was associated with later offending. A notable limitation in their study include a sample based solely on females in prison, whose experiences with abuse and later offending may differ from females who are not incarcerated.

Additionally, they used a single question to measure abuse and were unable to account for frequency of abuse experiences or severity of abuse. However, these results provide support for both a life course/developmental perspective and a feminist pathways framework. In short, feminist pathways researchers identify victimization, MHPs, and substance abuse as central risks for female offenders (Belknap & Holsinger, 2006; DeHart & Moran, 2015; Gehring, 2018).

Furthermore, Baglivio, Wolff, Piquero, DeLisi, and Vaughn (2018) reported that males experienced changes in domains related to family and peer relations, school, mental health, skills for dealing with emotions and feelings, and aggression. In contrast, female youths’ prominent domains were program-supervised tasks and substance abuse. Improved domain scores for both males and females resulted in reduced recidivism, but the effect was greater for female youth.

Accordingly, to better serve justice-involved youth, it may be essential to consider their gender- specific needs and responsivity when both assessing risk and providing programming.

Gender Responsive Risk Assessments

Claims that risk factors (Covington, 2007) and criminogenic needs (Vitopoulos et al.,

2012) are assumed to be uniform for males and females may indicate that the RNR model is gender-neutral. Risk assessment research may also be gender neutral; according to Hoge et al.

(2013), much of the risk assessment literature pertains to American, British, and Canadian males.

Hence, information derived from risk instruments for female youth is limited. Risks and needs for female offenders are also often evaluated based on risk instruments developed with mostly male samples (Barnes et al., 2016). Gender-neutral risk instruments may not account for the

context of female offending, as these tools were developed following male theories of delinquency (Chesney-Lind, 2001). Some researchers have instead supported gendered pathways, stating that these differential trajectories should be evaluated when identifying risk and protective factors for males and females (Schwalbe, Fraser, & Day, 2007).

Yet, other researchers argue that certain risk instruments, such as the LSI-R and other LS instruments, are predictive of female recidivism (Andrews et al., 2012; Smith, Cullen, & Latessa,

2009; Olver et al., 2014). Although these researchers found that the LS assessments were predictive of female reoffending, it is important to note that these results do not automatically translate to all risk instruments. Additionally, and as noted by Andrews et al. (2012), much of the predictive validity for females was attributed to the substance abuse domain. Following from a feminist pathways perspective (Daly, 1992), it could be that substance abuse is a proxy for other female-specific risk/need factors, such as trauma (Broidy et al., 2018).

Additionally, researchers who support gender-neutral instruments argue that gender- specific factors are accounted for with the inclusion of items that act as proxies, such as offenders’ attitudes and peer associations (Bonta & Andrews, 2017). As an example, predictive validity does not vary across gender in the YLS/CMI (Hoge & Andrews, 2011; Jung & Rawana,

1999). Schwalbe (2008) also found that gender did not impact predictive validity and that the strongest predictors, including a history of antisocial behavior and antisocial attitudes, peers, and personality, are the same across gender (Schwalbe, 2008).

In contrast, Hubbard and Pratt (2002) contend that a history of sexual and/or physical abuse is more important in predicting recidivism for females than it is for males. Other researchers have assessed gender specificity by developing risk models for gender-neutral and gender-specific samples. For instance, Hamilton et al. (2016) created the Static Risk Offender

Need Guide for Recidivism (STRONG-R) utilizing dynamic and static items, multivariate selection, gender-specific models and analytically weighted items. They compared their assessment to a variety of other assessments (e.g., those using only static items, gender-neutral instruments, assessments based on bivariate selection, and assessments with unweighted items).

The authors found that the female STRONG-R model outperformed several models by 2 to 9%: a bivariate-only model; an assessment that was gender-neutral and included only static items, an assessment with unweighted items, bivariate selection and that was gender neutral; and a gender- neutral assessment with only static items and that had unweighted items and bivariate selection

(p. 251). Results from such studies suggest that there is a small increase in predictive performance with gender-specific modeling.

Gender-responsive instruments may also be necessary, as female and male youth have different risk profiles (Cottle et al., 2001; Flores et al., 2004; Funk, 1999; Leiber & Mack, 2003;

Schwalbe, 2008; Schwalbe, Fraser, Day, & Cooley, 2006; Thompson & McGrath, 2012).

Consequently, inclusion of gender-specific items can predict different criminogenic trajectories for females and males (Van Voorhis, Wright, Salisbury, & Bauman, 2010; Brennan, Breitenbach,

Dieterich, Salisbury, & Van Voorhis, 2012), and assessments that are gender-neutral may overestimate risk for female youth, resulting in lower predictive accuracy and harsher dispositions (Leiber & Mack, 2003; Schwalbe, 2008; Schwalbe, Hatcher, & Maschi, 2009). In short, gender-neutral assessments may not be as effective at predicting female recidivism because they have lower predictive validity than gender-responsive instruments.

Gender-neutral assessments can also limit considerations of how gendered distinctions affect rehabilitative practices, while gender-specific assessments may provide practitioners with more information about a youth (Hannah-Moffat, 2009). The consequences of a predominant

male perspective also impact interventions for youth. For instance, many of the crimes that female youth are arrested for, including truancy, substance abuse, and running away, are associated with symptoms of abuse (Coalition for Juvenile Justice, 2013). Sexual abuse is one of the top predictors of females’ juvenile justice system involvement, and it has one of the largest effects on females’ continued contact with the system (Chesney-Lind, 1997; Conrad, Placella,

Tolou-Shams, Rizzo, & Brown, 2014; Hubbard & Pratt, 2002). The high prevalence of sexual abuse reported by female youth is a concern, as the juvenile justice system lacks trauma- informed care, which may increase the likelihood that survivors of abuse will be retraumatized.

Also, female youth who have a trauma history and who are detained may experience suicidal ideation or engage in self-harm (Kerig & Ford, 2015). Additionally, Kerig and Schindler (2013) found that interventions were less efficacious for female youth as a result of the masculine orientation underpinning those programs. Therefore, a gender-neutral approach to risk assessments may undermine use of gender-specific interventions. In turn, validity and responsivity can be affected.

The type of outcome measured by an RNA may also warrant a gender-specific approach, as experiences of trauma, MHPs, and substance use can influence the types of crime females commit. Researchers have found that girls with a history of sexual abuse are more likely to engage in more serious delinquent behavior (Leve, Chamberlain, & Kim, 2015). Additionally, property crime might be more prevalent for female offenders who use substances to cope with trauma experiences, as they resort to this crime type to support their drug using (DeHart, Lynch,

Belknap, Dass-Brailsford, & Green, 2014; Gilfus, 1993). Females with abuse experiences, and who evidence an early onset of criminal behavior, may be more likely to perpetrate violence crimes because they have had to protect themselves in abusive environments and relationships

(Broidy et al., 2018). Accordingly, an evidence-based approach to youth risk assessments that considers differences between male and female offenders might be more effective, and as indicated previously, trauma is a salient feature in the feminist pathway perspective.

Gender Differences with Traumatic Experiences

Research suggests that female youth experience more individual ACEs than males

(Baglivio & Epps, 2016; Baglivio et al., 2014; Wood, Foy, Layne, Pynoos, & James, 2002). The type of ACEs youth experience and their impact differ across genders. For instance, female youth experience higher rates of sexual abuse and interpersonal victimization (Baglivio et al.,

2014; Dierkhising et al., 2013; Wood et al., 2002), while male youth have witnessed more violence. However, when composite scores are considered, female and male youth experience similar rates of trauma (Baglivio et al., 2014).4

Moreover, males with ACEs are more likely to act violently and engage in delinquency

(Baglivio et al., 2014; Chiu, Ryan, & Herz, 2011). As an example, male youth who experience

ACEs are 35 to 144% more likely to perpetrate violence, while females are 38 to 88% more likely (Duke et al., 2010, p. e783). Conversely, female youth have less involvement in the juvenile justice system and are more likely to demonstrate INT behaviors, engage in self- mutilation, or experience mental health symptoms (Baglivio et al., 2014; Leadbeater, Blatt, &

Hertzog, 1999). Female youth who experience maltreatment also have a higher likelihood of co- occurring delinquency and substance use, while males do not (Widom & White, 1997). Still, regardless of gender, greater exposure to ACEs results in a higher likelihood of negative outcomes, such as violent behavior (Duke et al., 2010).

4 A current deficit in ACEs research regards exposure to ACEs and their impact on youth with varying sexual orientations and/or genders, such as youth who identify as lesbian, gay, bisexual, queer/questioning, intersex or asexual.

Additionally, females who experience maltreatment are more likely to perpetrate violent offenses later in life compared to females without such an experience (Widom & Maxfield,

2001). Herrera and McCloskey (2001) also discovered that females who have experienced childhood physical abuse were arrested more for violent offenses compared to males who also had experiences of physical abuse. Outcome differences following exposure to different ACE types, as well as divergent outcomes between female and male youth suggests differing pathways in offending and a necessity for gender-specific analyses when researching ACEs (Baglivio et al., 2016). These findings also demonstrate that experiences of trauma should be considered when examining risks and needs, as ACEs contribute to prediction of reoffending and may also constitute a need that could be addressed in treatment.

Mental Health Issues and Gender Differences

Rates of mental health concerns vary by gender, with females experiencing higher rates of mental health issues (Teplin et al., 2002). Skowyra and Cocozza (2007) report that 81% of female youth have any MHP, whereas, 66.8% of male youth do (p. 3). Moreover, the relationship between trauma and mental health concerns is particularly evident for female youth. Following trauma exposure, females are more likely to develop MHPs than males (Crimmins, Cleary,

Brownsteing, Spunt, & Warley, 2000).

As mentioned previously, Broidy et al. (2018) discovered that abuse experienced by girls was related to INT behaviors. Additionally, Goodkind, Ng, and Sarri (2006) found that female youth offenders who had a history of sexual abuse reported more substance abuse, poorer mental health, greater engagement in risky sexual behavior, and more delinquent behavior as compared to female youth who had not been sexually abused. Contextualized within the RNR framework, female youth offenders have greater mental health needs and more negative responses to juvenile

justice involvement (Teplin et al., 2002; Wasserman, McReynolds, Ko, Katz, & Carpenter 2005).

However, girls tend to be undertreated for these needs, which may result in a greater likelihood of offending (Wasserman et al., 2005).

INT and EXT disorder gender variations. Female youth are more likely to exhibit depression, anxiety, PTSD, bipolar behaviors, and other mood disorder symptoms (Espinosa,

Sorensen, & Lopez, 2013; Kashani et al., 1987). Conversely, male youth tend to exhibit EXT disorders (Espinosa et al., 2013; Kazdin, 2005), which present as a threat to others and the community (Kazdin, 2005), increasing a male youth’s likelihood of having contact with the juvenile justice system. Furthermore, gender socialization can also result in disparate manifestation of CD systems. For instance, antisocial boys affiliate with like-peers who reinforce deviant behavior (Maccoby, 1986; 1990); yet, girls with EXT behaviors are often rejected by peers and are punished for their behaviors (Keenan, Loeber, & Green, 1999). Experiences of trauma may also contribute to differential appearance of mental health symptoms.

King and colleagues (2011) found that experiences of sexual abuse were associated with

ADHD, disruptive disorders, and substance use in males but anxiety and affective disorders in females. However, these authors discovered that physical abuse was related to affective disorders for males but ADHD, disruptive disorders, and substance use for females. As such, females and males might have different pathways to INT and EXT disorders. In regard to co-occurring disorders, female youth with co-occurring issues have a greater likelihood of reoffending while on community supervision compared to their male counterparts (McReynolds, Schwalbe, &

Wasserman, 2010). Past scholars have also discussed the necessity of gender-specific interventions, in part due to the higher prevalence of trauma histories and co-occurring MHPs exhibited by female youth (Smith & Saldana, 2013).

Moreover, multiple youth and family characteristics differentially mediate the relationship between maltreatment and MHPs for females and males. For instance, an absence of family cohesion was related to depression in maltreated males, but family conflict was associated with depression in females who experienced maltreatment (Meyerson, Long, Miranda, & Marx,

2002). Identification of mediators may contribute to gender-specific treatment.

Gender-Specific Treatments

Treatment provision can also be gender-neutral if services are provided based on criminogenic needs, but a gender-specific approach may be necessary since female youth have different needs and prevalence of behaviors (e.g., prostitution, substance abuse, running away) that constitute a greater harm to themselves than others (Hubbard & Matthews, 2008). As trauma can have a large effect on female pathways to offending (Broidy et al., 2018), interventions following a trauma-informed approach can result in greater recidivism reductions. An example of a trauma-informed programs includes the trauma recovery and empowerment model (TREM;

Harris & Anglin, 1998). An offshoot of TREM has been developed specifically for girls and young women and involves a group setting: G-TREM. Trauma-focused cognitive-behavioral therapy (Najavits, Weiss, Shaw, & Muenz, 1998) may also be used. Essentially, these types of interventions attempt to avoid re-traumatizing individuals (Bowie, 2013) and can instead treat symptoms of trauma rather than interpreting problem behaviors resulting from traumatic experiences as deviant.

Furthermore, females categorized as high-risk by gender-neutral RNAs may receive stricter sanctions that exacerbate their needs (Holtfreter & Morash, 2003). Importantly, gender should be considered a responsivity factor, particularly because need categories in RNAs do not reflect the differential life experiences of females (Vitopoulos et al., 2012). As such, limiting

interventions to traditional criminogenic needs, based on male offenders, may neglect issues contributing to female offending (Covington, 1998).

Summary

In this chapter, a theoretical background, by way of life course and developmental theories, underpinning youth offending was provided. The RNR model, and how this framework is used in the juvenile justice system via risk assessments, was next described. The PACT was specifically discussed, as it is used in Washington State and work. Furthermore, past research has demonstrated relationships between (1) ACEs and offending; (2) ACEs and MHPs; and (3) mental health issues and offending. It is important to distinguish between INT and EXT disorders within these relationships, as these mental health concerns may differentially interact with ACEs and disparately contribute to youthful deviant behavior, with co-occurring disorders being the most likely to affect youths’ outcomes. Furthermore, programming was discussed to demonstrate that treatment can work, and it can benefit both the individual youth and the community at large. Gender differences were also highlighted to better elucidate how all the topics discussed previously could also be viewed within a gender responsive framework and to improve juvenile justice interventions with female youths.

The Current Study

This study aims to identify youth offenders’ needs and whether they are provided with programming that matches those needs. Additionally, the use of mediation analyses to examine how programming affects the relationship between (1) ACEs and recidivism and (2) MHPs and reoffending for male and female youth will further test the effect of service provision on reducing recidivism. Figure 1 provides a visual representation regarding how these concepts may be associated. This study will further assess differences between youth with INT, EXT, and co-

occurring symptoms in their recidivism outcomes. Life course theory will also be investigated within the context of ACEs and MHPs, where differences between early-onset and late-onset youth will be examined. Ultimately, this research provides a larger picture of youth offending and how trauma and mental health issues, which have yet to be thoroughly researched, may constitute a risk, need, and/or potential responsivity factor for justice-involved youth.

Consideration of ACEs and MHPs may better inform practitioners regarding disposition and program placement decisions for youth offenders.

Figure 1. Visual Representation of Related Concepts. This figure illustrates how the concepts under study may be associated with each other.

CHAPTER THREE RESEARCH METHODOLOGY The primary intent of this study was to examine the effect of programming on youth recidivism, as it pertains to need-service matching, as well as the indirect effects of ACEs and

MHPs on the association between programing and reoffending. A secondary goal of the current research was to examine how programming differentially impacts male and female youths’ recidivism. A third objective of this study involved an examination of how youth with INT,

EXT, or co-occurring symptoms differed in their reoffending rates, as well as how programming impacted their recidivism. The final purpose of this study was to assess life course theory and whether ACEs and MHPs predicted onset of deviant behavior in addition to whether onset predicted different types of recidivism. The following sections describe the research questions, the risk/needs instrument used to collect data on youth offenders, case inclusion criteria, measures included in the analyses, and the analytic plan.

Research Questions

The research questions for this study concerned justice-involved youths’ needs in addition to their responsivity to programming.

1. Are need-service matches more effective in reducing recidivism? a. Are racial/ethnic minority youth disproportionately represented in either of the need-service groups? b. What youth characteristics predict their need-service group? c. Do program-eligible youth with need-service matches differ from those with mismatches on different recidivism outcomes? 2. Do ACEs (1) affect youth recidivism on their own and (2) does programming indirectly affect the relationship between ACEs and reoffending? a. Is this effect the same between male and female youth? 3. Do mental health concerns affect the relationship between gender and recidivism? 4. Does programming indirectly affect the relationship between MHPs and reoffending? a. Is this effect the same between male and female youth? b. Is this effect the same among youth with INT, EXT, or co-occurring symptoms?

c. Which youth characteristics, and other variables, are predictive of recidivism for youth with INT, EXT, or co-occurring symptoms? 5. Are ACEs and/or reported MHPs associated with onset of deviant behavior? a. Does age of onset differentially predict the three types of recidivism? Data To fulfill the study aims, data was gathered from the Washington State Center for Court

Research (WSCCR) in the Administration Office of the Courts (AOC). Data concerning youth characteristics, PACT responses, programming eligibility and participation, and recidivism came from the AOC. The following subsections provide greater detail about the data, including sample characteristics, predictors, and the outcomes.

Sample. The current study enlisted a purposive sample of probation youth collected by the AOC. Eligibility criteria were as follows: (1) a misdemeanor or felony charge; (2) an adjudication; (3) a disposition of community probation or supervision; and (4) administration of the PACT Full Assessment. The sample included both male and female youth involved in the

Washington State juvenile justice system from December of 2003 to June of 2017. The final sample size was 50,862 justice-involved youth.

This sample was further partitioned into a male-only subset, which was approximately

75% of the sample, and a female-only subset (25%). The total sample was also be divided into two subsets: youth without mental health issues (approximately 64% of the sample) and those with mental health symptoms (36%). Finally, the subset of youth with documented mental health symptoms were further partitioned into youth with either: (1) INT symptoms (29.3%); (2) EXT symptoms (11.5%); or (3) co-occurring internal and external disorders (4.4%). Table 2 depicts the descriptive statistics of the sample.

Table 2. Sample Descriptives Measures Total (n = 50,862) Male (n = 38,100) Female (n = 12,762) % % % Race/ethnicity White (reference) 61.0 60.5 62.3 African American 13.9 13.9 13.8 Latino/a 15.5 16.5 12.6 Other 9.6 9.1 11.3 Risk Level Low 4.0 4.0 3.9 Moderate 38.8 38.5 39.6 High (reference) 57.2 57.5 56.5 Age (windsorized) 12 and under 2.3 2.5 1.7 13 6.2 6.2 6.0 14 12.9 12.6 14.0 15 21.3 20.6 23.4 16 25.5 25.4 25.9 17 25.7 26.2 24.1 18 and older 6.0 6.4 4.9 Current MHPs ------INT 29.3 27.4 35.1 EXT 11.5 12.5 8.5 Co-Occurring 4.4 4.7 3.8 Substance use No use 35.9 35.9 35.7 Use, no life disruption 18.2 18.3 18.1 Use, life disruption 45.9 45.8 46.2 Recidivism ‘Any’ offense 60.0 63.3 50.2 Any felony 26.2 30.0 14.9 Any violent 25.7 28.2 18.4 Any non-violent 61.1 42.0 29.6

Predictors. Several predictors were created from PACT items, necessitating further discussion of the assessment. The PACT includes static and dynamic items that constitute risk and protective factors. The social history aspect of the PACT is gathered by youth probation officers, and criminal history items are collected from reports on youths’ criminal behavior.

Youth are initially assessed with the PACT (see Appendix A) when they are brought to the court for a new complaint or adjudication to supervision under county probation (Barnoski, 2004c).

Youth may be administered the PACT Prescreen and/or Full Assessment. The latter involves a

structured interview with youth and their families to determine risks and needs5. Youth are reassessed every six months to measure their progress in programming and supervision. If needed, the case plan is adjusted to account for any change in risk or need; consequently, risks and needs may change across assessments. Youth are administered a final assessment while still on supervision.

As discussed in Chapter Two, the Full Assessment has 126 items measuring 12 domains.

For data analyses, items with categorical responses or ‘select all that apply’ were recoded as binary responses. For instance, one item from the Current Alcohol and Drugs domain pertains to alcohol use. There are seven responses the assessor can select from (e.g., no use or use is not disrupting functioning, disrupts education, causes family conflict, interferes with prosocial friendships, causes health problems, and/or contributes to criminal behavior). Multiple responses may be selected for a maximum score of 11 points. For each item response, a binary variable was created (‘no’ = 0, ‘yes’ = 1). Furthermore, protective items were recoded as negative values, as the domain scores are created by subtracting protective scores from risk scores to create a ‘buffer score’. For example, one question in the Mental Health History domain regards history of MHPs.

Youth who do not have a history of MHPs received a ‘-1’, indicating a protective response.

Sample descriptives for PACT responses are provided in Appendix B.

To assess recidivism rates across youth who had program matches and mismatches, four groups were created based on Washington State’s program eligibility for evidence-based practices (see Appendix C). The first step to creating these groups was to determine whether youth had a need via whether they were eligible for programming. Eligibility was based on

5 Low-risk youth may also receive the Full Assessment, as juvenile justice system actors have discretion to overrule recommendations indicated in the PACT.

youths’ responses to PACT items; as such, it was possible to determine whether they have a need in different domains (e.g., education/employment, family, mental health, substance use, history of violence, aggression, attitudes/behaviors, and skills). Mental health and substance abuse needs/treatment are not included in the Washington State program eligibility; therefore, two items from the PACT were used to determine whether a current need exists: ‘current mental health problem status’ from the Mental Health History domain and ‘youth using alcohol/drugs’ from the Alcohol and Drug History domain.

Once the presence of needs was established (‘no’ = 0, ‘yes’ = 1), a binary variable regarding program start was created (‘no’ = 0, ‘yes’ = 1). Programs include Education and

Employment Training (EET), FFT, FIT, MST, ART, mental health treatment, and alcohol and other drug (AOD) treatment. FIT and MST were combined into one variable, as FIT is based off

MST. Program start, rather than completion, was used, as it indicates at least some dosage of the programming. Youth may not complete programming for a multitude of reasons (e.g., a new offense resulting in institutionalization, parents refusing to allow youth to continue, youth moves, etc.). As such, program start allows for a more comprehensive picture of youth who have received some programming.

Next, the two need-service groups were created. Youth who either had a need and received programming matching that need, or youth without any needs and who did not receive programming, were categorized as a ‘need-service match’. For instance, if a youth had a history of violence or aggression and received ART, then he or she would be in this group. Conversely, youth who had a need but received programming that did not match it (e.g., a substance abuse need, but he or she received ART) were designated as a ‘need-service mismatch’. Youth who

had no need but received programming (e.g., no substance abuse issues but received AOD treatment) were also identified in the ‘mismatch’ group.

Furthermore, an ACEs indicator was created from items in the PACT. Creation of ACEs scores are based on Baglivio and colleagues’ (2014) identification and coding of ACEs from

PACT items, which can be found in Appendix D. Current research typically uses an ACE composite score, where the youth’s total number of ACEs, with a range of zero to ten, is calculated to capture their overall exposure to childhood adversity. To create this score, each individual ACE is dichotomized (yes/no), and a youth need only experience the event once to register a ‘yes’, even if he or she was exposed multiple times as a child (Baglivio et al., 2014).

Moreover, since ACEs are highly interrelated (Baglivio & Epps, 2016), another ACE measure was created to identify youth who had fewer than four ACEs (‘0’) and those who had four or more ACEs (‘1’). Past research has shown that a threshold of four ACES is useful, where children who exceed three ACEs have more negative outcomes as adults (Felitti et al., 1998).

A flag for mental health issues was also created to identify youth with reported mental health symptoms (‘no’ = 0, ‘yes’ = 1). Within the mental health issue subset, binary variables (0

= ‘no’, 1 = ‘yes’) were constructed to display whether youth had INT, EXT, or co-occurring symptoms. Youth that reported symptoms of depression or somatic complaints were classified as

‘INT’, whereas youth with a reported history of ADHD were categorized as ‘EXT’. Youth with symptoms from the INT and EXT group were designated in the ‘co-occurring’ group.

An indicator for onset of deviant behavior was also constructed based on one PACT response. The first question in the Criminal History section asks about youths’ age at first offense. The highest risk response for this item is ‘under 13’; accordingly, youth whose first

offense was prior to age 13 were considered ‘early-onset’ (0) while all other youths were categorized as ‘late-onset’ (1).

Outcomes. The primary outcome for the current study was recidivism, which was defined as a charge or complaint occurring within 24 months of a youth’s initial assessment, that results in a new adjudication. Multiple reoffending outcomes were examined: ‘any’ (any misdemeanor or felony), violent, and non-violent (property or drug recidivism), which do not represent mutually exclusive categories. For instance, a given youth could have both a violent and non-violent offense. Presence of mental health symptoms were also used in multiple analyses to examine whether ACEs were predictive of INT, EXT, and co-occurring symptoms.

Additionally, age of onset was utilized as an outcome to explore whether ACEs and MHPs were predictive of onset of deviance.

Analytic Plan

This study used several analytic techniques to answer the research questions outlined above. Tests for need-service matching were first performed to identify whether need-service matching, at a broad level, helped reduce youth recidivism risk. Two types of needs considered to be non-criminogenic, ACEs and MHPs, were then examined more closely to explore whether they affect recidivism. Moreover, the responsivity of youths with ACEs or MHPs was also assessed to ascertain whether these needs (and potentially responsivity factors) impact continued recidivism by evaluating whether programming for these youth resulted in decreased reoffending. Lastly, life course theory was explored by first examining relationships amongst

ACEs, MHPs, and age of onset as well as testing whether age of onset was associated with the recidivism outcomes. The following sections detail each analysis utilized to address the research questions.

Need-service matching and disparate treatment. First, to explore whether youth of a racial/ethnic minority were disparately represented in either the need-service match or mismatch group, a relative rate index (RRI) was calculated to examine the magnitude of any disparity present. This analysis allows for a comparison of the rate of Black, Hispanic, or ‘other’ youth to

White youth in order to assess whether racial/ethnic minorities are less likely to receive programming matching their needs. Past research has demonstrated that racial/ethnic minorities are not as likely to receive treatment or utilize services despite having greater needs, particularly mental health needs (Rawal, Romansky, Jenuwine, & Lyons, 2004). Consequently, an RRI allows for an assessment of disparity in need-service matching, were a value greater than one is indicative of disparity while a score of one represents equivalent levels of either need-service matching or mismatching.

Next, variables predictive of need-service group were examined to identify other youth attributes that potentially contribute to service assignment. For this research question, a binary logistic regression was performed to assess whether mental health symptomology, in addition to other youth characteristics, predicted need-service matches versus mismatches. Youth attributes found to be significant predictors of a mismatch may be indicative of disparate treatment within this sample of youth.

Lastly, propensity score weighting (PSW) was utilized to balance the need-service match and mismatches groups on multiple youth attributes to make the two groups more comparable when evaluating their recidivism differences. PSW involves four steps; first, predicted probabilities, or propensities, of youths’ need-service (mis)match were estimated via a binary logistic regression. Furthermore, to test sensitivity and specificity, a Receiver Operating

Characteristic (ROC) curve was assessed to examine the predictive accuracy of the model. The

ROC produces an Area Under the Curve (AUC) statistics, which has a range of 0.5 to 1.0 and represents a percentage of accuracy. The effect sizes of AUC prediction strength are displayed in

Table 3. These tests are performed pre- and post-weighting, where a substantial reduction in the

AUC following weighting provides evidence that the groups have been balanced on the covariates.

Table 3. AUC Industry-Standard Effect Size Ranges6 <.55 Negligible >.55 Small >.63 Moderate >.71 Strong

The second step of PSW was conducted by doing one divided by the propensity score of treatment subjects (need-service matches). The inverse (1/1-propensity score) was calculated for comparison youth (mismatches). The next step involved standardization of the weight by dividing each group’s weight by their respective group mean propensity score to minimize the influence of outlying scores. Lastly, diagnostics were completed to assess whether PSW balanced the two groups, wherein a box-plot of predicted probability was examined. Bivariate tests were also conducted to explore covariate means between the match and mismatch groups pre- and post-weighting. After balancing the two groups, chi-square tests were calculated to examine recidivism differences for youth eligible for each programming type to better ascertain whether need-service matches for each program (e.g., EET, FFT, FIT/MST, ART, mental health treatment, and AOD) resulted in lower reoffending than need-service mismatches.

ACEs as non-criminogenic needs. The next series of analyses involved ACEs. The prevalence of ACEs for the sample was first calculated, followed by a frequency check for the

6 See Rice and Harris (2005) for a review.

subset of youth with MHPs. After identification of ACEs, a binary logistic regression with the composite ACEs score, along with other control variables, was performed to examine whether

ACEs contribute to increased odds of reoffending. Additionally, in order to explore associations between ACEs, programming, and recidivism, Spearman’s ρ correlations were assessed for the total, male-only, and female-only samples. Examination of these relationships offers preliminary evidence for the proposed models, in that a significant relationship between ACEs and recidivism indicates that there is a path to be mediated. Stated otherwise, these correlations demonstrate that a portion of the impact of youth’s ACEs on recidivism can be explained by the effect of ACEs on programming. The measures for these analyses were non-linear; as such,

Spearman’s ρ was utilized instead of Pearson’s correlation because the former provides a nonparametric measure.

Following these bivariate analyses, only relationships that exhibited a significant relationship between ACEs and a specific programming type were tested further in a mediation model. Past research has demonstrated that certain programming (e.g., family-based and ART) does not interact with ACEs to decrease youths’ odds of recidivism (Kowalski, 2018). The present study employed a mediation procedure to build upon these relationships and explore whether programming mediated the relationship between ACEs and recidivism. In other words,

ACEs may result in youth being more likely to receive certain types of programming because they have a past family concern. Programming might then result in a change of youth’s likelihood of reoffending. The mediation analysis can then test whether the influence of the mediator (programming) is stronger than the direct effect of ACEs (the independent variable;

Hayes, 2018).

Essentially, this analysis allows for an examination of whether programming participation outweighs the negative effects of ACEs while statistically controlling for those experiences (see Figure 2). Bootstrapping was also utilized to examine the direct effects of ACEs and programming on reoffending, in addition to the indirect effect of ACEs on recidivism by way of programming. Bootstrapping is a nonparametric method of resampling that generates a sampling distribution of results (Hayes & Preacher, 2010). The confidence interval (CI) for the indirect effect can be estimated from the distribution. For the current study, p values were not utilized to determine significance of the indirect effect, as Baron and Kenny’s (1986) procedure for mediation tests with dichotomous variables assumes a normal distribution. The present study used a bootstrap of 5,000 draws, which has been recommended by Hayes (2018); the bootstrap procedure results in a bias-corrected 95% CI for each estimate. If zero is present within the CI, there is evidence that the indirect effect is not significant. These analyses were performed for the total, male, and female samples.

Figure 2. Programming and ACEs Mediated Path Model. This figure illustrates the direct and indirect path of ACEs on youth reoffending.

Gender and MHPs as responsivity factors. Next, to address the third research question regarding the effect of MHPs on the relationship between gender and recidivism, mediated path analyses were performed (see Figure 3). These analyses help identify whether different types of

MHPs differentially impact the path between gender and reoffending and can help inform treatment provision by identifying gender and MHPs as potential responsivity factors. However,

correlations were first tested to assess whether both gender and MHPs were associated with recidivism. Three analyses were then completed for the three mental health groups – INT, EXT, and co-occurring symptoms. The indirect effect of these analyses was examined. Bootstrapping was also utilized to examine the direct effects of gender and MHPs on reoffending, in addition to the indirect effect of gender on recidivism by way of MHPs.

Figure 3. Gender and MHPs Mediated Path Model. This figure illustrates the direct and indirect paths of gender and MHPs on recidivism.

MHPs as non-criminogenic needs. Another set of mediated analyses was conducted to assess the indirect effect of MHPs on recidivism by way of programming and the direct effect of

MHPs on recidivism (see Figure 4). Bootstrapping was again enlisted to identify the 95% CI around point estimates. Mediated path analyses were performed to explore the effect of programming on the relationship between INT symptoms and recidivism, EXT symptoms and reoffending, and co-occurring symptoms and recidivism for the total, male-only, and female-only samples.

Figure 4. Programming and MHPs Mediated Path Model. This figure presents the direct and indirect paths of MHPs on youth recidivism.

Binary logistic regressions were next completed to examine recidivism in the INT, EXT, and co-occurring symptoms subsets for the total, male, and female samples. Several predictors

were included to ascertain which youth attributes were predictive of reoffending for youth with

INT, EXT, or co-occurring symptoms. Essentially, these analyses may provide a rationale for mental health-specificity. Similar to gender specificity, such tests might indicate that certain predictors are differentially related to reoffending across INT, EXT, and co-occurring symptoms.

ACEs, MHPs, and age of onset. Lastly, life course theory was tested. However, relationships amongst ACEs, MHPs, and age of onset were first examined to ascertain whether

ACEs were associated with an early age of onset in addition to whether different types of MHPs were disparately related to onset of deviance via correlations. Then, age of onset was utilized as a predictor in logistic regressions to examine whether early-onset and late-onset youth vary in their

‘any’, violent, and non-violent recidivism. Findings, and further discussion of these analyses, are presented in the following chapter.

CHAPTER FOUR

FINDINGS

This chapter presents the results of the current study, which employed multiple analyses to test the proposed research questions from the preceding chapter. These analyses set out to explore the effect of need-service matching, ACEs, mental health issues, and age of onset on youth recidivism. Additionally, the impact of programming on the relationship between (a)

ACEs and recidivism and (b) MHPs and reoffending were examined.

Differences in need-service matching across youth were first tested, followed by an assessment of recidivism outcomes between a balanced sample of youth with matches or mismatches. The prevalence and effect of ACEs on youth reoffending were then explored, and the indirect effect of programming on the relationship between ACEs and recidivism was tested.

Next, the impact of MHPs on youth reoffending was assessed. First, the indirect effect of

INT, EXT, and co-occurring symptoms on the relationship between gender and recidivism was explored to identify both gender- and mental health-specific results. Following these tests, the impact of youth programming on the relationship between MHPs and reoffending was examined to better identify whether specific MHPs present as potential responsivity factors to treatment.

Then, mental health specificity was explored via multivariate tests to model the relationship between youth characteristics and recidivism for youth with INT, EXT, or co-occurring symptoms. Lastly, life course theory was assessed. The relationship between ACEs, MHPs, and age of onset were first examined followed by a test of the effect of age of onset on reoffending.

Results are organized by research question, as outlined in the methods section. The findings are summarized before discussing the limitations and implications of the results presented.

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Effect of Needs-Service Matching on Recidivism

The first set of research questions concerned need-service matching and potential disparities in service assignment in addition to differences in recidivism between youth with matches and mismatches. Specifically, this section sought to address whether racial/ethnic youth were more likely to receive mismatches through RRIs, which youth characteristics were related to mismatches via a logistic regression, and chi-square tests to examine whether youth with mismatches were more likely to recidivate. It was anticipated that mismatches would be more prevalent in minority youth, that youth attributes (e.g., gender, age, risk class) would be differentially related to mismatches, and that youth with mismatches would have a higher recidivism likelihood. Prior to conducting these analyses, youths’ needs and programming eligibility were identified. Then, two need-service groups were created to denote whether youth had either a need-service match or mismatch.

As mentioned in the previous chapter, youths’ programming eligibility was determined by following EBP eligibility requirements created by Washington State (see Appendix C). Need- service groups were identified by first determining whether youth were eligible for services and then ascertaining whether they received services in line with those needs. Table 4 displays information pertaining to youths’ programming eligibility and participation in addition to their need-service group. The highest need across samples involved aggression, as approximately 90% of youth qualified for ART. Examinations of programming start first involved limiting the samples to youth who were eligible for a given program. The most-attended programming was

EET, followed by ART and AOD treatment. Lastly, most youth were found to have a need- program mismatch (63%). Overall, while these descriptives indicate that over 50% of youth were

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eligible for each service, apart from mental health treatment, many of these needs were not addressed by an appropriate service.

Table 4. Program Eligibility and Start for Youth Measure Total (n = 50,862) M(SD) Program eligibility EET .72(.45) FFT .58(.49) FIT/MST .49(.50) ART .91(.28) Mental health .09(.29) AOD .56(.50) Program started for eligible youth EET .49(.51) FFT .03(.16) FIT/MST .09(.28) ART .33(.47) Mental health .05(.21) AOD .22(.42) Need-service group Need-program match .37(.48) Need-program mismatch .63(.48)

To better examine individual factors potentially affecting need-service assignment, the subsequent analyses explore the rate of matching across White and racial/ethnic minority youth.

Representation of Racial/Ethnic Groups in Need-Service Groups

Presence of racial/ethnic disparity pertaining to need-service matching was next assessed.

The first part of research question one involved potential racial/ethnic disparity in service assignment for youths’ needs. RRIs, which assess the magnitude of bias at different decision- making points, were utilized to identify disparity in need-service matching between White and minority youth in addition to the magnitude regarding any differences. First, however, the study sample was compared to the general population in Washington State.

In Table 5, the current sample was compared to the 2017 Integrated Public Use

Microdata Series (IPUMS) database (Ruggles et al., 2018). The IPUMS data for the US includes microdata collected from 15 federal censuses as well as from the American Community Surveys

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(ACS). The samples incorporate information from every available census from 1850-2010 and the 2000-Present ACS samples. A comparison to the general Washington State youth population is included to indicate whether minority youth are disproportionately represented in the sample, and the results suggest that both Black and Hispanic youth were disproportionately represented.

Further analyses were performed to investigate whether racial/ethnic youth were overrepresented in different need-service groups. RRIs were calculated to explore this possibility.

Table 5. Comparison of the WA Sample to 2017 IPUMS, Ages 6 to 20 Race/Ethnicity WA Probation % 2017 IPUMS % White 61.0 61.0 Black 13.9 3.0 Hispanic 15.5 9.2 Other 9.6 26.8

The RRIs shown in Table 6 demonstrate differences in need-program group by race/ethnicity. Compared to White youth, Black youth were 16% less likely to have a need- program match but an 11% greater likelihood of a mismatch. Similarly, Hispanic youth demonstrated an 11% decreased likelihood of a match relative to White youth but a 7% increased likelihood of a mismatch. Lastly, youth in the ‘other’ group had a 20% lesser likelihood of receiving a match and a 13% higher likelihood of a mismatch relative to White youth.

Table 6. Population-Based Relative Rate Index for Need Program Groups Need Program Group White Black Black Hispanic Hispanic Other Other Youth % Youth % RRI Youth % RRI Youth % RRI Need-program match 39.6 33.1 .84 35.2 .89 31.5 .80 Need-program mismatch 60.4 66.9 1.11 64.8 1.07 68.5 1.13 Total n 29,036 6,536 -- 7,327 -- 4,554 --

These findings indicate potential disparity in assigning services to youths’ needs as a function of their race/ethnicity; yet, it is important to note that these differences are not substantial. The next set of analyses explores possible disparity in need-service matching further by considering additional youth characteristics.

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Youth Attributes Predictive of Need-Service Group

The second portion of the first research question concerned whether different youth attributes were predictive of need-service (mis)matching. Further testing was conducted to explore other characteristics, apart from race/ethnicity, that were associated with need-service matching. This examination serves to indicate other youth subsets less likely to receive an intervention in line with their needs, including distinctions across age and between males and females, risk level classifications, or mental health concerns. Findings have implications regarding equitable treatment and potential bias within the juvenile justice system. Importantly, these results cannot show whether potential disparity is explicit or implicit, but the findings serve to help juvenile justice staff reflect on their decision-making or assess their resource allocation to determine which youth groups are being underserved. A binary logistic regression (match = 0, mismatch = 1) was performed to assess this question.

As displayed in Table 7, youth characteristics were predictive of need-program group (p

< .001). Bolded ORs indicate significant relationships. Despite being significant, all effects were small in magnitude7. However, the AUC was greater than .63, which represents a moderate effect size (Rice & Harris, 2010), indicating that these youth attributes are good predictors of need- service group. Regarding significant predictors, increased age was associated with greater odds of having a need-program mismatch (OR = 1.22, p < .001), while males had 8% increased odds of a mismatch (OR = 1.07, p < .001). Furthermore, compared to White youth, Black (OR = 1.26),

Hispanic (OR = 1.17), and other (OR = 1.31) youth all had greater odds of receiving a mismatch

(p < .001). Both low (OR = .16) and moderate (OR = .80) risk youth had a significantly lower

7 Compared to Cohen’s d effect sizes, where .2 is considered small, .5 medium, and .8 large, an OR of 1.68 is small, 3.47 medium, and 6.71 large. Chen, Cohen, and Chen (2010) provide a review.

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odds of having a need-program mismatch when compared to high risk youth (p < .001). Lastly, the only significant mental health predictor was presence of INT symptoms, wherein youth had

62% decreased odds of receiving a need-program mismatch compared to youth without such symptomology. Accordingly, age, gender, race/ethnicity, risk, and mental health status disparity is evident in allocation of services matching youths’ needs.

Table 7. Predictors of Need-Program Mismatches and Matches, N = 47,236 Model Fit ᵪ2 = 2,788.07*** AUC = .653 Measures B SE OR Age .20 .01 1.22*** Gender -.08 .02 .92*** Race/ethnicity White (ref.) Black .23 .03 1.26*** Latino/a .16 .03 1.17*** Other .27 .04 1.31*** Risk Low -1.84 .05 .16*** Moderate -.22 .02 .80*** High (ref.) Internalizing -.47 .02 .62*** Externalizing .06 .04 1.07 Co-Occurring -.08 .06 .93 Note: OR = Odds Ratio; ***p < .001, **p < .01.

The following section addresses the impact of mismatching on recidivism. The results in

Table 7 demonstrate that certain youth are less likely to receive a need-service match, which is in part a result of their specific characteristics. Consequently, if mismatches result in greater recidivism, then we are potentially setting up certain youth, purely as a function of their attributes rather than their recidivism risk, to engage in continued deviance by not providing them with appropriate services.

Weighted Need-Program Match and Mismatches on Reoffending

The final tests for research question one concerned recidivism differences between youth with matches and mismatches. Prior to testing this effect, a PSW was implemented to balance the

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two groups on youth attributes, which previous analyses demonstrated were differentially related to likelihood of a mismatch. This weighting process can help reduce selection bias between the samples. As part of the PSW process, a binary logistic regression with multiple covariates was conducted to obtain the AUC for the model in order to (a) ascertain whether the variables are good predictors of need-service matching and (b) compare the pre-weighting with the post- weight AUC examining whether selection bias reduced. A PSW procedure was then executed to compute weights, which were standardized to reduce the impact of outlying scores. Diagnostics were next performed to determine whether the weighting procedure balanced the need-program mismatch and match groups, as measured by the AUC. Lastly, the standardized weight was used to assess the recidivism outcomes. Following PSW, chi-square tests were performed to examine recidivism differences between youths with matches and mismatches.

Table 8 presents findings for balancing the need-service match and mismatch groups. The

AUC for the model pre-PSW was 0.65, indicating that the covariates were moderate predictors for need-program group (Rice & Harris, 2005). Prior to weighting, 12 of 13 variable means were significantly different between the two need-program groups. Subsequent to weighting, there were 2 significant differences (p < .05) and one marginal difference (p < .1). Moreover, the AUC dropped to a small effect size (.51), offering evidence that selection bias was substantially reduced. Stated otherwise, the two groups were identified as balanced, post-weighting.

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Table 8. Need-Program (Mis)Match Balancing Statistics Before PSW After PSW Measures Need-Program Need-Program Need-Program Need-Program Match Mismatch Match Mismatch (n = 17,680) (n = 29,773) M(SE) M(SE) T M(SE) M(SE) t Age 15.39(.01) 15.76(.01) -27.24*** 15.59(.01) 15.60(.01) -1.07 Gender .75(.00) .75(.00) 1.14 .75(.00) .75(.00) .41 Race/ethnicity .66(.01) .78(.01) -12.66*** .73(.00) .73(.00) -.10 Risk class 1.44(.00) 1.60(.00) -27.60*** 1.54(.00) 1.53(.00) 2.47* Internalizing .35(.00) .25(.00) 21.96*** .29(.00) .30(.00) -1.34 Externalizing .12(.00) .11(.00) 2.94** .12(.00) .12(.00) -.62 Co-Occurring .06(.00) .04(.00) 9.39*** .05(.00) .05(.00) -1.91† Program Eligibility EET .64(.00) .75(.00) -25.13*** .71(.00) .71(.00) .91 FFT .58(.00) .61(.00) -6.62*** .60(.00) .59(.00) 1.08 FIT/MST .48(.00) .52(.00) -9.22*** .50(.00) .50(.00) .76 ART .89(.00) .96(.00) -24.58*** .93(.00) .92(.00) 3.10** Mental health .04(.00) .05(.00) -5.00*** .05(.00) .05(.00) .55 AOD .46(.00) .42(.00) 9.97*** .44(.00) .43(.00) 1.29 AUC .653 .508 Note: ***p < .001, **p < .01, *p < .05, †p < .1.

As demonstrated in Table 9, after weights were implemented, differences concerning only violent recidivism were significant (p < .1). Notably, youth with matches had a higher violent reoffending mean8. Further analyses were conducted to better examine which types of program matches (e.g., EET, FFT, FIT/MST, ART, AOD, or mental health treatment) had the largest effect on recidivism reduction.

Table 9. Recidivism Outcomes by Study Group After PSW Outcome Need-Program Need-Program Match Mismatch Recidivism M(SD) M(SD) ᵪ2 Any .60(.49) .60(.49) .02 Violent .26(.44) .26(.44) 3.70† Non-violent .39(.49) .39(.49) .01 Note: ***p < .001, *p < .05, †p < .1.

As shown in Table 10, bolded results indicate that need-service matching was associated with reoffending. Youth eligible for EET and who had a need-service mismatch demonstrated

8 Prior to rounding, the average violent recidivism for youth with need-service matches was .2626 but .2570 for youth with mismatches.

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greater recidivism than youth with matches for any (p < .001) and non-violent reoffending (p <

.001). Furthermore, subsequent to weighting, youth eligible for FFT and who had mismatches demonstrated higher non-violent recidivism (p < .01) but lower violent reoffending (p < .05) compared to youth with matches. Additionally, youth eligible for FIT/MST and who had a mismatch displayed greater any (p < .01) and non-violent (p < .001) recidivism. Regarding ART- eligible youth, those with matches had higher rates of violent reoffending (p < .05). Additionally, youth eligible for mental health treatment and who had a match showed greater any (p < .1) and violent (p < .01) recidivism. Finally, all recidivism outcomes were significant post-weighting for youth eligible for AOD, wherein youth with mismatches indicated greater rates of any (p < .001), violent (p < .05), and non-violent (p < .001) reoffending. Together, these results indicate that matching sometimes results in greater recidivism compared to youth with need-service mismatches, particularly for youth who receive mental health services and/or if the recidivism outcome is violent reoffending. In contrast, need-service matching appears to be particularly effective for youth with education/employment and/or substance use needs.

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Table 10. Recidivism Outcomes by Study Group Program Eligibility After PSW Outcome Need-Program Need-Program Match Mismatch M(SD) M(SD) ᵪ2 EET (n = 31,179) Any .60(.49) .62(.49) 16.04*** Violent .26(.44) .26(.44) .14 Non-violent .38(.49) .39(.49) 15.51*** FFT (n = 26,148) Any .64(.48) .65(.48) 2.19 Violent .30(.46) .29(.45) 4.27* Non-violent .42(.49) .43(.50) 7.69** FIT/MST (n = 21,894) Any .65(.48) .67(.47) 8.25** Violent .31(.46) .31(.46) .32 Non-violent .43(.50) .45(.50) 12.14*** ART (n = 40,716) Any .61(.49) .61(.49) .03 Violent .28(.45) .27(.44) 6.42* Non-violent .40(.49) .40(.49) .01 Mental Health Treatment (n = 1,966) Any .65(.48) .62(.48) 3.36† Violent .34(.47) .30(.46) 6.83** Non-violent .43(.49) .45(.50) 2.15 AOD (n = 18,654) Any .64(.48) .66(.47) 20.26*** Violent .26(.44) .27(.44) 4.57* Non-violent .42(.49) .44(.50) 23.98*** Note: ***p < .001, **p < .01, *p < .05, †p < .1.

The above results pertaining to need-service matching indicated that there are potential disparities regarding which youth are more likely to receive a match. Notably, older youth, females, minority youth, and high-risk youth were less likely to receive a match, suggesting that certain youth characteristics affect the likelihood of a match and may be a sign of unequal treatment in the system. Moreover, an unexpected result concerned the relationship between need-service matches and greater recidivism likelihood. Specifically, youth eligible for mental health treatment and who had a need-service match were more likely to have any and violent reoffending. Analyses presented below address the relationship between MHPs and recidivism further and identify differences across youth with INT, EXT, and co-occurring symptoms. First, however, the prevalence and relationship of ACEs to recidivism and programming is explored to

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identify whether ACEs present as a substantial, non-criminogenic need that affects recidivism risk in addition to whether it impacts responsiveness to programming.

ACEs as Potential Needs and Responsivity Factors

The previous section demonstrated that need-service matching, particularly for youth who are eligible for AOD treatment, is an important factor in reducing recidivism. Yet, such matching was not efficacious for youths with MHPs. Additionally, these analyses did not include exploration of trauma as a need or services specifically intended to address trauma symptoms.

The current section focuses on ACEs, which present as a potential need and/or responsivity factor that were not targeted by any of the services examined.

Prevalence of ACEs

The second research question involved an examination of ACEs. The prevalence rates of

ACEs in the total, male, and female samples are displayed in Table 11. Across the three samples, parental separation or divorce was most common (82-88%), followed by family violence (70-

80%) and incarceration of a household member (65-72%). Chi-square tests were conducted to examine differences between males and females on each ACE indicator. All comparisons were significantly different (p < .001), with males reporting lower indications on all levels of ACEs.

Although significant, these variations are likely an artifact of the large sample size. Accordingly, effect sizes were also calculated to gauge the magnitude in differences.

Although females reported significantly higher exposure to all ACEs, they had substantively greater reporting of sexual abuse (41 vs. 14%), where females were nearly three times more likely to indicate past sexual abuse (d = .63). Additionally, although all other effect sizes fell within the small effect size range (d < .5), the findings demonstrate substantial differences between females and males on physical abuse (51 vs. 36%), family violence (80 vs.

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70%), and indication of four or more ACEs (81 vs. 65%). Conversely, results pertaining to emotional abuse and neglect, physical neglect, household substance use and MHPs, parental separation or divorce, and household family member incarceration were much smaller (d < .2).

Although the differences on these ACEs were smaller between males and females, the results clearly illustrate that females are consistently more afflicted by these life experiences and their potential consequences than males.

Table 11. Prevalence of ACEs for All Youth Total Males Females ᵪ2 (n = 50,862) (n = 38,100) (n = 12,762) ACE Indicator M(SD) M(SD) M(SD) Emotional abuse .53(.50) .52(.50) .54(.50) 32.86*** Physical abuse .40(.49) .36(.48) .51(.50) 861.74*** Sexual abuse .21(.41) .14(.35) .41(.49) 4,295.09*** Emotional neglect .35(.48) .33(.47) .39(.49) 129.11*** Physical neglect .26(.44) .24(.43) .32(.47) 351.71*** Family violence .73(.45) .70(.46) .80(.40) 522.02*** Household substance abuse .41(.49) .39(.49) .45(.50) 138.54*** Household MHPs .18(.38) .16(.37) .22(.41) 203.26*** Parental separation or divorce .84(.37) .82(.38) .88(.33) 195.66*** Household member incarcerated .67(.47) .65(.48) .72(.45) 158.35*** Four or more ACEs .69(.46) .65(.48) .81(.40) 1,061.24***

Differences in prevalence of ACE indicators across youth with or without MHPs were also explored to ascertain whether youth with MHPs were more likely to report traumatic experiences, which may present as a further need for this group. Findings are presented in Table

12. Youth with no MHPs demonstrated the lowest rate of all ACE indicators apart from emotional abuse (52%), household substance use (40%), and household family member incarceration (67%). However, these youth did not have the highest indication of any ACE measure. Youth with INT symptoms evidenced the highest rates of emotional abuse (54%), household substance use (43%), and household member incarceration (68%). Conversely, youth with EXT symptoms did not indicate the greatest prevalence of any single ACE; yet, both these youth and those with co-occurring symptoms had nearly equal experiences of parental separation

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or divorce (88%). Lastly, youth with co-occurring symptoms presented with the highest rates of physical (66%) and sexual (38%) abuse, emotional (41%) and physical (38%) neglect, family violence (92%), household MHPs (33%), and indication of four or more ACEs (85%).

Although small in magnitude (d < .5), substantial differences were found between youth with MHPs and those without on physical (d = .39) and sexual abuse (d = .27), family violence

(d = .43), household MHPs (d = .26), and four or more ACEs (d = .31). Effect size tests for youth with INT, EXT, or co-occurring symptoms revealed that all differences were small in magnitude.

However, despite being small in magnitude, substantial differences were found for the following comparisons: INT and co-occurring symptoms on physical abuse (52 vs. 66%), sexual abuse (28 vs. 38%), family violence (85 vs. 92%), and household MHPs (24 vs. 33%); and EXT and co- occurring symptoms on family violence (84 vs. 92%). The findings in this section demonstrate that ACEs are prevalent in the study sample, especially for youth with MHPs. The following section assesses the effect of ACEs on youth recidivism to identify whether these experiences present as a non-criminogenic need that is strongly related to recidivism.

Table 12. Prevalence of ACEs for MHP subsets No MHPs INT EXT Co-Occurring (n = 32,357) (n = 14,914) (n = 5,849) (n = 2,258) ACE Indicator M(SD) M(SD) M(SD) M(SD) Emotional abuse .52(.50) .54(.50) .52(.50) .51(.50) Physical abuse .33(.47) .52(.50) .59(.49) .66(.48) Sexual abuse .17(.37) .28(.45) .33(.47) .38(.48) Emotional neglect .33(.47) .39(.49) .39(.49) .41(.49) Physical neglect .23(.42) .30(.46) .36(.48) .38(.49) Family violence .66(.47) .85(.36) .84(.37) .92(.28) Household substance abuse .40(.49) .43(.50) .37(.48) .39(.49) Household MHPs .14(.34) .24(.43) .29(.46) .33(.47) Parental separation or divorce .82(.38) .86(.35) .88(.33) .88(.32) Household member incarcerated .67(.47) .68(.47) .65(.48) .67(.47) Four or more ACEs .64(.48) .78(.41) .81(.39) .85(.36)

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Effect of ACEs on Recidivism

Binary logistic were next preformed to answer the second research question concerning whether ACEs contributed to increased reoffending odds for the three recidivism measures. Past research has indicated a relationship between ACEs and recidivism in other states (Baglivio et al., 2014, 2015; Barrett et al., 2014a, 2014b; Dembo et al., 1993, 1995; Maxfield & Widom,

1996; Teague et al., 2008; Wolff & Baglivio, 2017); thus, this analysis aims to identify whether this relationship holds in Washington State. It was anticipated that ACEs would be associated with increased odds of ‘any’, violent, and non-violent reoffending. Results of these analyses are shown in Table 13. Bolded ORs indicate that ACEs were significantly related to recidivism.

Although these results were significant, they were typically small in magnitude, as the ORs were less than 1.68 (Chen et al., 2010). Yet, for the most part, AUC values were moderate in strength

(e.g., approximately .63 or greater; Rice & Harris, 2005). One exception concerned non-violent recidivism for the female subset, where the AUC was .61, suggesting that these variables were not as strong in predicting non-violent recidivism for girls as they were in other models.

More specifically, youths’ summary ACE score was associated with 2% increased odds of ‘any’ recidivism for the total sample (OR = 1.02, p < .001) and 3% greater odds for the male sample (OR = 1.03, p < .001). In contrast, ACEs were unrelated to ‘any’ recidivism in the female-only sample (OR = 1.00, p > .05). Furthermore, youths’ cumulate ACE scores were related to 4% increased odds of violent reoffending in the total and male samples (OR = 1.04, p

< .001). These results were similar for the female-only sample, where increases in the ACE score were associated with 4% higher odds of violent reoffending (OR = 1.04, p < .01). Findings also indicate that ACEs were associated with 2% increased odds of non-violent recidivism for the total sample (OR = 1.02, p < .01). The summary ACE score was also related to male reoffending,

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where ACEs resulted in 2% higher odds of recidivating (OR = 1.02, p < .001). However, ACEs were unrelated to female non-violent reoffending (OR = .99, p > .05).

Together, these results indicate that ACEs are a stronger predictor for male recidivism.

However, ACEs were associated with violent, female reoffending, which is an indication that

ACEs are a potentially strong risk factor for aggression or violence in females. As these findings illustrate a relationship between ACEs and recidivism, particularly for males, the next section sought to identify whether programming could help reduce the effect of these experiences on subsequent youth recidivism.

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Table 13. ACEs and Youth Recidivism Any Recidivism Violent Recidivism Non-Violent Recidivism Measures Total Males Females Total Males Females Total Males Females (n = 45,740) (n = 34,353) (n = 11,387) (n =45,740) (n = 34,353) (n = 11,387) (n = 45,740) (n = 34,353) (n = 11,387) ᵪ2 = 3,417.33*** ᵪ2 = 2,470.89*** ᵪ2 = 471.71*** ᵪ2 = 2,739.15*** ᵪ2 = 1,930.46*** ᵪ2 = 508.06*** ᵪ2 = 2,629.90*** ᵪ2 = 1,769.30*** ᵪ2 = 349.09*** AUC = .657 AUC = .654 AUC = .614 AUC = .657 AUC = .648 AUC = .650 AUC = .639 AUC = .628 AUC = .607 OR OR OR OR OR OR OR OR OR ACE score 1.02*** 1.03*** 1.00 1.04*** 1.04*** 1.04** 1.02** 1.02** .99 Male 1.73*** -- -- 1.80*** -- -- 1.75*** -- -- Age .96*** .98** .89*** .89*** .91*** .77*** .89*** .90*** .87*** Race/ethnicity White (ref.) ------Black 1.51*** 1.52*** 1.43*** 2.08*** 2.17*** 1.72*** 1.38*** 1.36*** 1.41*** Hispanic 1.55*** 1.68*** 1.17** 1.51*** 1.61*** 1.06 1.13*** 1.17*** .98 Other 1.12** 1.11* 1.14* 1.19*** 1.24*** 1.02 .98 .96 1.05 Risk class Low .25*** .24*** .29*** .22*** .22*** .22*** .27*** .26*** .28*** Moderate .54*** .52*** .61*** .55*** .53*** .64*** .58*** .57*** .62*** High (ref.) ------INT .72*** .67*** .84*** .89*** .86*** 1.03 .83*** .78*** .98 EXT 1.07† 1.05 1.18† 1.29*** 1.26*** 1.60*** 1.08* 1.09* 1.02

115 Co-Occurring 1.20** 1.35*** .84 1.26** 1.33*** .94 1.12† 1.23** .84 Current substance use

None .69*** .66*** .79*** .99 .97 1.19** .71*** .69*** .80*** Use/no disruption 1.11*** 1.13*** 1.04 1.16*** 1.17*** 1.10 1.06* 1.07* 1.04 Use/disruption (ref.) ------Note: OR = Odds Ratio; ***p < .001, **p < .01, *p < .05, †p < .1.

Effect of Programming on the Relationship between ACEs and Reoffending

The second research question also aimed to address whether services provided to justice- involved youth in Washington could ameliorate the effect of ACEs on recidivism. To answer this question, mediation analyses were performed to explore whether programming impacted the relationship between ACEs and recidivism. Bivariate relationships were first examined via

Spearman’s ρ correlations to ascertain whether there were associations to be mediated. The findings from these tests are first presented, followed by the results of the mediation analyses.

Bivariate results. Significant correlations amongst youths’ ACE scores, whether they started programming, and whether they had any recidivism at 24 months were first tested.

Further examination was conducted only if a significant correlation existed between (1) ACEs and recidivism and (2) program start and recidivism, as both indicate the potential for a significant indirect relationship to exist between ACEs, programming, and reoffending. The only programming to meet these conditions was AOD treatment, wherein the total, male, and female samples resulted in the requisite significant associations. For the total sample, the composite

ACE score was significantly related to both AOD treatment (ρ = -.03, p < .01) and recidivism (ρ

= .03, p < .01) for youth eligible for AOD treatment. Additionally, AOD treatment was associated with recidivism (ρ = -.03, p < .01). Similar results were evidenced for the male-only sample. Again, ACEs were associated with AOD treatment (ρ = -.04, p < .01) and reoffending (ρ

= .07, p < .01). AOD and recidivism were also significantly related (ρ = -.03, p < .01).

Relationships were slightly different for the female-only sample. Although ACEs were associated with recidivism (ρ = .03, p < .05), they were not related to AOD participation. As with the total and male sample, AOD treatment was significantly associated with recidivism (ρ = -.05,

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p < .01). Overall, these findings indicate that ACEs and AOD treatment were related to changes in youth recidivism. The following section better illustrates the nature of these associations.

Mediation results. Findings from the mediation analyses are next displayed, where the unstandardized results for the models are presented in Table 14. For the total sample, greater

ACE scores were associated with a lower likelihood of pariticipating in substance use treatment

(b = -.020, p < .001). They were also related to increased recidivism while controlling for substance use treatment (b = .020, p < .001). Conversely, AOD treatment was related to decreased recidivism while controlling for ACEs (b = -.061, p < .001). The indirect effect was signifciant, indicating that treatment participation mediated the effect of ACEs on recidivism (b

= .001, p < .01). ACEs accounted for .2% of the variance in AOD particticpation. Both ACEs and AOD treatment explained .5% of the variance in youth reoffending.

For males, ACEs were associated with a decreased likelihood of participating in AOD treatment (b = -.027, p < .001) but were related to an increased likelihood of recidivism while controlling for programming (b = .044, p < .001). However, AOD programming was associated with a lesser likelihood of reoffending (b = -.045, p < .01). The indirect effect was significant (b

= .001, p < .005), but the bootstrapped CI included zero (CI [.00, .02]), so this effect should be interpeted with caution. Based solely on statistical significance, AOD treatment mediated the relationship between ACEs and recidivism. While ACEs explained .3% of the variance in AOD programming, both ACEs and AOD treatment accounted for .1% of the variance in reoffending.

Unlike the total and male samples, ACEs for females were not related to AOD treatment participation (b = -.012, p > .05). They were, however, associated with a greater likelihood of recidivism while controlling for the effect of AOD programming (b = .023, p < .05). Also, AOD treatment participation was associated with a decreased likelihood of recidivating while

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controlling for the effect of ACEs (b = -.080, p < .01). The indirect effect was non-significant (b

= .001, p > .05), indicating AOD programming did not mediate the relationship between ACEs and reoffending for girls. ACEs explained .1% of the variance in AOD treatment participation, while ACEs and AOD programming accounted for .9% of the variance in female reoffending.

Table 14. Mediation Analyses – ACEs, AOD & Recidivism Effects Total Males Females (n = 25,853) (n = 19,424) (n = 6,429) Direct effects AOD on ACEs -.020*** -.027*** -.012 Recidivism on ACEs .020*** .044*** .023* Recidivism on AOD -.061*** -.045** -.080** Indirect effect Recidivism via AOD & ACEs .001** .001* .001 Note: ***p < .001, **p < .01, *p < .05.

These findings indicate that AOD treatment is more effective in reducing the effect of

ACEs on recidivism but only for males. Stated otherwise, males who report ACEs are more responsive to AOD treatment than similarly situated females. Furthermore, this section has shown that ACEs present as both a need and responsivity factor for justice-involved youth in

Washington. The following sections first seeks to identify whether MHPs differentially affect the relationship between recidivism for males and females and then address whether MHPs also function as needs and responsivity factors but with a specific focus on the differences between

INT, EXT, and co-occurring symptoms.

Effect of MHPs on the Relationship between Gender and Recidivism

The third research question involved the effect of MHPs on the relationship between gender and recidivism. A greater understanding regarding the interplay between gender and specific types of mental health concerns could help inform correctional interventions. It was anticipated that females would be more likely to report INT symptoms while males would demonstrate a higher likelihood of EXT and co-occurring symptoms. It was also expected that all

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MHPs would mediate the relationship between gender and recidivism. Prior to exploring these relationships, correlations between gender, MHPs, and recidivism were examined to ensure there were relationships to be mediated. To be included as a model, a significant relationship had to exist between gender and recidivism as well as between MHPs and recidivism. INT (r = -.06) and EXT (r = .02) symptoms were related to ‘any’ recidivism (p < .01). Co-occurring symptoms were not; however, they were associated with violent reoffending (r = .04, p < .01). Thus, for the co-occurring model, violent recidivism was used at the outcome. Moreover, gender was associated with both ‘any’ (r = .12) and violent (r = .10) recidivism (p < .01).

INT Symptoms as a Mediator

As shown in Figure 5, both gender and presence of INT symptoms were directly associated with ‘any’ reoffending, where males had a higher likelihood of recidivating while controlling for the direct effect of INT symptoms (p < .001). Conversely, youth with INT symptoms had a decreased likelihood of reoffending while controlling for gender (p < .001).

Moreover, males were less likely to have INT symptoms than females (p < .001). The indirect effect was significant (p < .001), offering evidence that presence of INT symptoms mediated the relationship between gender and recidivism.

Figure 5. Effects of Gender and INT Symptoms on Recidivism

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EXT Symptoms as a Mediator

Additionally, Figure 6 demonstrates that males had a greater recidivism likelihood while controlling for the direct effect of EXT symptoms (p < .001), as did youth with EXT symptoms while controlling for the direct effect of gender (p < .001). Additionally, males were more likely to have symptoms compared to females (p < .001). The indirect effect was significant (p < .001), indicating that these symptoms mediated the relationship between gender and reoffending.

Figure 6. Effects of Gender and EXT Symptoms on Recidivism Co-Occurring Symptoms as a Mediator

As shown in Figure 7, males exhibited a greater likelihood of having co-occurring INT and EXT symptoms (p < .01). They were also more likely to demonstrate violent recidivism while controlling for the direct effect of co-occurring MHP status (p < .001). Co-occurring

MHPs were associated with reoffending while controlling for the direct impact of gender (p <

.001). The indirect effect was significant (p < .01), indicating that co-occurring INT and EXT symptoms mediated the association between gender and recidivism. Overall, these findings indicate that MHPs affect the relationship between gender and recidivism. Again, these results help to inform programming decision-making. The next section addresses how INT, EXT, and co-occurring symptoms more specifically impact youth recidivism and how programming may help to decrease the effect of MHPs on reoffending.

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Figure 7. Effects of Gender and Co-Occurring Symptoms on Recidivism

Mental Health Symptoms as Needs and Responsivity Factors

The fourth research question regarding the impact of MHPs on reoffending was next assessed. This set of analyses focused on an examination of the indirect effect programming had on the relationship between MHPs and reoffending to better identify whether youth differ in their responsivity to treatment. Prior to conducting mediation analyses to explore these relationships,

Pearson’s correlations were first examined to ascertain whether there were significant relationships between INT, EXT, co-occurring symptoms, programming, and recidivism. If significant associations existed between MHPs and reoffending, as well as between programming and recidivism, then they were assessed via mediated path analyses.

Bivariate Relationships

Again, only AOD treatment was associated with recidivism, so the below models do not address other programming types. INT (r = -.06, p < .01) and EXT (r = .03, p < .01) were associated with ‘any’ reoffending, but co-occurring symptoms were not. Instead, the relationship between co-occurring symptoms and violent recidivism was found to be significant (r = .03, p <

.01). Consequently, ‘any’ recidivism was utilized in the model for INT and EXT symptoms, while violent reoffending was used for co-occurring symptoms. Additionally, AOD treatment was related to both ‘any’ (r = -.03, p < .01) and violent (r = -.04, p < .01) recidivism.

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Furthermore, INT (r = -.06, p < .01) and EXT (r = .03, p < .01) symptoms were related to any recidivism for males. Co-occurring symptoms were associated with only violent reoffending

(r = .03, p < .01). Also, AOD treatment was significantly related to both any (r = -.03, p < .01) and violent (r = -.03, p < .01) recidivism for the male-only sample. INT (r = -.03, p < .05) and

EXT (r = .04, p < .01) symptoms were also related to reoffending for females. Additionally, co- occurring symptoms were associated with violent recidivism (r = .03, p < .05). AOD treatment was significantly related to any (r = -.05, p < .01) and violent (r = -.06, p < .01) reoffending.

Mediation Analyses

Findings for the mediation analyses are next presented. Results pertaining to INT symptoms, AOD treatment, and ‘any’ recidivism are shown in Table 15. For the total sample, youth with INT symptoms were more likely to participate in AOD treatment (b = .162, p < .001).

Both INT symptoms (b = -.164, p < .001) and treatment particpation (b = -.058, p < .001) were related to a decreased likelihood of ‘any’ recidivism. The indirect effect was significant (b = -

.009, p < .001), suggesting that AOD programming mediated the effect of INT symptoms on youth recidivism. INT symptoms and AOD treatment accounted for .9% of the variance in reoffending while INT symptoms explained .5% of the variance in AOD programming.

For the male sample, presence of INT symptoms was related to an increased likelihood of

AOD treatment (b = .161, p < .001). Again, INT symptoms (b = -.166, p < .001) and programming (b = -.046, p < .01) were associated with a decreased likelihood of recidivism. The indirect effect was significant (b = -.007, p < .01), suggesting that AOD treatment mediated the relationship between symptoms and male reoffending. The two predictors accounted for .8% of the variance in recidivism, and symptoms explained .5% of the variance in AOD programming.

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In the female sample, presence of INT symptoms was associated with AOD treatment participation (b = .149, p < .001). INT symptoms (b = -.074, p < .05) and AOD programming (b

= -.079, p < .01) were related to a decreased likelihood of reoffending. Lastly, the indirect effect was significant (b = -.012, p < .05), providing evidence that AOD treament mediated the relationship between INT symptoms and female recidivism. INT symptoms and AOD programming explained .8% of the variance in reoffending, and INT symptoms accounted for

.5% of the variabiliy in AOD treatment participation. These findings appear to indicate that AOD treatment is effective for youth with INT symptoms, which may demonstrate that INT symptoms are a protective responsivity factor.

Table 15. Mediation Analyses – INT, AOD & Recidivism Effects Total Males Females (n = 21,946) (n = 16,619) (n = 5,327) Direct effects AOD on INT symptoms .162*** .161*** .149*** Recidivism on INT symptoms -.164*** -.166*** -.074* Recidivism on AOD -.058*** -.046** -.079** Indirect effect Recidivism via AOD & INT symptoms -.009*** -.007** -.012* Note: OR = ***p < .001, **p < .01, *p < .05.

Moreover, as shown in Table 16, presence of EXT symptoms did not affect the likelihood of AOD treatment participation for the total sample (b = .033, p > .05). However, EXT status was associated with increased recidivism while controlling for the effect of AOD programming

(b = .157, p < .001). In contrast, AOD treatment participation was related to a decreased likelihood of reoffending while controlilng for the direct effect of EXT symptoms (b = -.063, p <

.001). The indirect effect was not significant, indicating that participation in AOD treatment did not impact the relationship between EXT symptoms and recidivism (b = -.001, p > .05). While

EXT symptoms did not explain any of the variance in AOD programming, both EXT symptoms and AOD treatment accounted for .6% of the varibility in reoffending.

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Furthermore, EXT symptoms were not related to AOD participation (b = .045, p > .05) in the male sample. Presence of symptoms was associated with a higher likelihood of recidivism while controlling for the effect of programming (b = .127, p < .001). In contrast, treatment participation was related to a decreased recidivism likelihood while controlling for the impact of

EXT symptoms (b = -.051, p < .01). The indirect effect was not significant (b = -.002, p > .05); consequently, treatment did not mediate the effect between EXT symptoms and reoffending.

While EXT symptoms and AOD programming accounted for .4% of the variance in male recidivism, EXT symptoms explained none of the variance in AOD treatment participation.

In the female subset, EXT symptoms were not associated with AOD treatment for females (b = -.049, p > .05). While symptoms were related to increased recidivism (b = .169, p <

.05), programming was associated with reduced reoffending (b = -.081, p < .01). The indirect effect was not significant (b = .004, p > .05), demonstrating that AOD treatment did not mediate the relationship between EXT symptoms and female reoffending. Both predictors accounted for

.9% of the variance in recidivism, but EXT symptoms did not explain any of the variance in programming. Unlike the INT models, EXT symptoms are not a protective factor for youth; rather, they seem to be a risk factor that is not effectively addressed by AOD treatment.

Table 16. Mediation Analyses – EXT, AOD & Recidivism Effects Total Males Females (n = 21,946) (n = 16,619) (n = 5,237) Direct effects AOD on EXT symptoms .023 .045 -.049 Recidivism on EXT symptoms .157*** .127*** .169* Recidivism on AOD -.063*** -.051** -.081** Indirect effect Recidivism via AOD & EXT symptoms -.001 -.002 .004 Note: ***p < .001, **p < .01, *p < .05.

Findings for co-occurring symptoms are shown in Table 17. For the total sample, co- occurring symptoms were associated with an increased likelihood of participating in AOD

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treatment (b = .144, p < .01). Co-occurring symptoms were also related to an increased likelihood of recidivism while controlling for the direct effect of AOD programming (b = .232, p

< .001). In contrast, participation in treatment was associated with a decreased likelihood of recidivism (b = -.080, p < .001). The indirect effect was significant (b = -.011, p < .05), indicating that AOD programming mediated the relationship between co-occurring symptoms and reoffending for the total sample. Co-occurring symptoms explained .1% of the variance in

AOD treatment, and both predictors accounted for .8% of the variance in reoffending.

In the male sample, co-occurring symptoms were associated with AOD treatment (b =

.157, p < .05). While these symptoms were related to increased reoffending (b = .218, p < .001), programming was associated with reduced recidivism (b = -.066, p < .001). The indirect effect was significant (b = -.010, p < .05), indicating that AOD treatment mediated the effect between co-occurring symptoms and recidivism. Both predictors accounted for .6% of the variance in reoffending while co-occurring symptoms explained .1% of the variance in programming.

For the female subset, co-occurring symptoms were not associated with AOD treatment

(b = .107, p > .05). However, they were related to increased recidivism controlling for the effect of AOD treatment (b = .245, p < .05). The confidence interval for this estimate contains zero (CI

[-.004, .462]), which suggests that this relationship is not substantively significant. Programing was associated with a decreased likelihood of reoffending (b = -.120, p < .001). The indirect was not significant (b = -.013, p > .05), thus providing evidence that AOD treatment did not mediate the relationship between co-occurring symptoms and female recidivism. Both co-occurring symptoms and AOD programming explained 1.6% of the variance in reoffending, but co- occurring symptoms did not account for any of the variance in AOD treatment.

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Similar to the INT models, AOD treatment appears to be effective in reducing reoffending for youth with co-occurring symptoms even though these symptoms are related to increased recidivism. Furthermore, the results from these mediation analyses indicate that MHPs, particularly INT and co-occurring symptoms, are potentially both needs and responsivity factors for justice-involved youth in Washington State. The next section sought to identify other youth attributes that are predictive of recidivism for INT, EXT, and co-occurring subsets to provide potential, additional evidence supporting a framework of mental health specificity.

Table 17. Mediation Analyses – Co-Occurring, AOD & Recidivism Effects Total Males Females (n = 21,946) (n = 16,619) (n = 5,237) Direct effects AOD on co-occurring symptoms .157* .157* .107 Recidivism on co-occurring symptoms .020*** .218*** .245* Recidivism on AOD -.061*** -.066*** -.120*** Indirect effect Recidivism via AOD & co-occurring symptoms .001** -.010* -.013 Note: ***p < .001, **p < .01, *p < .05.

Multivariate Differences in Reoffending across MHPs

This section sought to answer the last part of the fourth research question: which youth attributes are predictive of recidivism for the INT, EXT, and co-occurring subsets of youth? Such specificity in identifying youth with different mental health concerns can inform treatment interventions. Binary logistic regressions were also performed to explore which variables were predictive of reoffending. It was expected that youth attributes would be differentially related to recidivism across the mental health subsets. This set of tests was conducted with only the total sample, as gender-specific models were untenable due to small sample sizes, particularly for females. However, gender was still included as a predictor in the models.

Bolded ORs in Table 18 indicate that ACEs were associated with recidivism. ACEs are specifically highlighted, as the above results demonstrated that these experiences are needs and

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responsivity factors in their own right. Although not explored in the present study, it may be the case that reporting of both ACEs and specific MHPs might compound youths’ needs and potential responsivity to treatment. Again, despite these ORs being significant, they are small in effect size, as they are less than 1.68 (Cohen et al. 2010). However, the AUC value for each model is approximately .63 or greater, indicating that the independent variables are moderately predictive of recidivism for youth with these mental health symptoms (Rice & Harris, 2005).

Specifically, cumulative ACE scores were only associated with ‘any’ recidivism for youth with INT symptoms (OR = 1.02, p < .05). However, males evidenced higher odds of reoffending for the INT, EXT, and co-occurring groups (OR = 1.59-1.99, p < .001). Conversely, increases in age were related to decreased odds of recidivism across the three subsets (OR = .84-

.90, p < .001). Both Black (OR = 1.41-1.56, p < .05) and Hispanic (OR = 1.38-1.68, p < .05) youth displayed greater odds of reoffending in the INT, EXT, and co-occurring groups. Risk class was also significant, where low (OR = .22-.23, p < .001) and moderate (OR = .42-.52, p <

.001) risk youth had lesser odds of recidivating compared to high risk youth in all mental health subsets. Additionally, youth with no current substance use exhibited decreased odds of reoffending compared to youth with current use and life disruption in all three groups (OR = .66-

.68, p < .01). Yet, only youth with INT symptoms and who reported current use and no life disruption demonstrated higher odds of recidivating compared to youth with use and life disruption (OR = 1.16, p < .05).

For the violent recidivism models, ACEs were related to greater odds of reoffending for the INT and EXT groups (OR = 1.04, p < .05). Again, males demonstrated higher recidivism odds (OR = 1.42-1.69, p < .01) while age was associated with lower odds (OR - .84-.88, p <

.001) for all three subsets. Similar to the ‘any’ recidivism model, Black (OR = 1.78-1.97, p <

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.001) and Hispanic (OR = 1.27-1.41, p < .1) exhibited increased odds of reoffending for youth in the INT, EXT, and co-occurring groups. Risk was again significant, where low (OR = .17-.29, p

< .01) and moderate (OR = .41-.53, p < .001) had decreased odds of recidivating compared to high risk youth in all three groups. However, only youth with EXT (OR = 1.18, p < .05) or co- occurring (OR = 1.30, p < .05) symptoms and who had no substance use evidenced greater reoffending odds compared to youth with use and life disruption. In contrast, youth with INT symptoms and substance use without life disruption had higher odds of recidivism compared to youth with use and life disruption (OR = 1.20, p < .01).

Lastly, ACEs were not associated with higher, non-violent recidivism odds for any of the

MHP subsets. Males, again, demonstrated greater reoffending odds (OR = 1.60-2.25, p < .001) while older youth had lower odds of recidivism (OR = .85-.89, p < .001). Dissimilar to previous models, only Black youth in the INT and EXT models (OR = 1.28, p < .01) displayed higher odds of reoffending compared to White youth. However, Hispanic youth in the co-occurring model had greater non-violent recidivism odds than White youth (OR = 1.50, p < .05).

Furthermore, as was the case with other models, low (OR = .22-.26, p < .001) and moderate (OR

= .50-.55, p < .001) exhibited decreased reoffending odds compared to high risk youths. Lack of substance use was related to decreased reoffending for the three mental health subsets (OR = .60-

.65, p < .001). Overall, these findings indicate that many of the examined youth attributes are predictive of recidivism regardless of whether youth have INT, EXT, or co-occurring symptoms.

However, a key exception appears to be ACEs, which were associated with ‘any’ and violent recidivism for youth with INT symptoms and then violent reoffending for youth with EXT symptoms. Again, this information can be used to inform correctional interventions.

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The next section is dissimilar from preceding sections, as it aims to test a theory, specifically life course theory. Although the analyses presented are a test of life course theory, similar to the above examinations, results derived from these tests can still inform programming for justice-involved youth in Washington State. In addition to exploring the relationships amongst ACEs, MHPs, and age of onset, the following section also examines the relationship between onset of deviance and recidivism.

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Table 18. Mental Health Subsets and Youth Recidivism Any Recidivism Violent Recidivism Non-Violent Recidivism Measures INT EXT Co-Occurring INT EXT Co-Occurring INT EXT Co-Occurring (n = 11,268) (n = 5,137) (n = 1,646) (n = 11,268) (n = 5,137) (n = 1,646) (n = 11,268) (n = 5,137) (n = 1,646) ᵪ2 = 889.22*** ᵪ2 = 374.96*** ᵪ2 = 167.89*** ᵪ2 = 651.40*** ᵪ2 = 236.29*** ᵪ2 = 125.07*** ᵪ2 = 724.12*** ᵪ2 = 331.68*** ᵪ2 = 141.96*** AUC = .660 AUC = .658 AUC = .682 AUC = .656 AUC = .629 AUC = .659 AUC = .648 AUC = .644 AUC = .667 OR OR OR OR OR OR OR OR OR ACE score 1.02* 1.03 1.01 1.04*** 1.04* 1.00 1.02 1.01 .98 Male 1.59*** 1.74*** 1.99*** 1.69*** 1.42*** 1.55** 1.60*** 2.06*** 2.25*** Age .89*** .90*** .84*** .84*** .88*** .85*** .87*** .89*** .85*** Race/ethnicity White (ref.) ------Black 1.44*** 1.56*** 1.41* 1.87*** 1.78*** 1.97*** 1.28*** 1.28** .81 Hispanic 1.38*** 1.48** 1.68* 1.27*** 1.41** 1.40† 1.03 1.13 1.50* Other 1.06 1.19 .94 1.08 1.21 1.13 .95 1.20 .89 Risk class Low .22*** .23*** .22*** .17*** .29*** .25** .22*** .26*** .24** Moderate .52*** .47*** .42*** .53*** .53*** .41*** .53*** .55*** .50*** High (ref.) ------Current substance use None .66*** .66*** .68** 1.02 1.18* 1.30* .65*** .62*** .60*** Use/no disruption 1.16* 1.09 1.02 1.20** 1.16 1.24 1.08 1.06 1.04

130 Use/disruption (ref.) ------Note: OR = Odds Ratio; ***p < .001, **p < .01, *p < .05, †p < .1.

Test of Life Course Theory

Life course theory was tested next. The final research question concerned the associations amongst ACEs, MHPs, and age of onset. Information regarding these relationships can inform youth programming, as the above results have indicated that both ACEs and MHPs are potential needs and responsivity factors. If these experiences are also related to onset of deviance, then services may be implemented that consider age, traumatic experiences, and mental health concerns. Pearson’s correlations were utilized to examine these associations. It was expected that these youth attributes would be related. Following these analyses, the effect of age of onset was explored in binary logistic regressions to assess whether youth with an early age of onset were more likely to recidivate. Past research has indicated that youths with an earlier age of onset are more likely to have persistent offending (Moffitt, 1993); thus, it was anticipated that age of onset would be associated with all three reoffending types.

Relationship between ACEs, current MHPs, and Age of Onset

Correlations were tested to explore the relationships amongst ACEs, MHPs, and age of onset. These bivariate examinations allow for an assessment of whether these needs are associated with age of onset, which might help to inform case management. As shown in Table

19, ACEs and age of onset were negatively related, indicating that each additional experienced

ACE was associated with a decreased likelihood of a later age of onset (p < .01). Conversely,

INT symptoms and onset of deviance were positively related, illustrating that reports of INT symptoms were associated with a greater likelihood of late onset (p < .01). However, presence of

EXT or co-occurring symptoms were related to an increased likelihood of an early onset (p <

.01). Taken together, these results indicate that ACEs, EXT symptoms, and co-occurring symptoms are all potential risk factors for an early age of onset while INT symptoms are

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somewhat protective. Yet, due to concerns related to temporal ordering, this conclusion cannot be fully supported in the present study. In short, ACEs and MHPs may contribute to an early age of onset, which itself has been connected to persistent deviance (Moffitt, 1993).

Table 19. Correlations between ACEs, MHPs & Age of Onset Measure ACEs INT EXT Co-Occurring Onset ACEs 1.00 INT .17** 1.00 EXT .12** .07** 1.00 Co-Occurring .10** .36** .60** 1.00 Onset -.07** .03** -.07** -.02** .48** Note: **p < .01.

Relationship between Age of Onset and Recidivism

The final analysis involved ascertaining whether an early age of onset was associated with recidivism in the present sample. Bolded results in Table 20 indicate that age of onset was related to all types of reoffending. Despite the significance of these results, the effect of an early age of onset appears to be small (Cohen et al., 2010). However, the AUC values are greater than

.63 for all models, suggesting that the variables included are moderately strong predictors of youth recidivism (Rice & Harris, 2005).

Specifically, youth who engaged in deviance at a later age demonstrated 19% lesser odds of ‘any’ recidivism (OR = .81, p < .001) for the total sample. Moreover, a late age of onset was associated with 12% decreased odds of violent reoffending (OR = .88, p < .001). Finally, youths with a later onset of deviance demonstrated 12% reduced odds of non-violent recidivism (OR =

.88, p < .001). In sum, as is the case with past research (Farrington, 2006; Hoeve et al., 2008;

Moffitt, 1993, 2006; Sampson & Laub, 1990, 1993), these findings support the notion that early onset of deviance behavior is related to continued deviance.

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Table 20. Age of Onset and Youth Recidivism, N = 45,735 Any Violent Non-Violent Recidivism Recidivism Recidivism ᵪ2 = 3,484.71*** ᵪ2 = 2,759.94*** ᵪ2 = 2,655.01*** AUC = .658 AUC = .658 AUC = .640 OR OR OR Age of onset .81*** .88*** .88*** Male 1.71*** 1.78*** 1.01** Age .97*** .89*** .90*** Race/ethnicity White (ref.) ------Black 1.51*** 2.08*** 1.38*** Hispanic 1.55*** 1.51*** 1.13*** Other 1.11** 1.18*** .97 Risk class Low .26*** .22*** .28*** Moderate .56*** .56*** .59*** High (ref.) ------ACE score 1.02*** 1.04*** 1.01** INT .72*** .90*** .83*** EXT 1.06 1.28*** 1.08* Co-Occurring 1.21** 1.26** 1.13† Current substance use None .67*** .98 .70*** Use/no disruption 1.10** 1.15*** 1.05† Use/disruption (ref.) ------Note: OR = Odds Ratio; ***p < .001, **p < .01, *p < .05, †p < .1. Summary

The findings of this study address multiple aspects of the juvenile justice system, including need-service matching, gender differences, ACEs, MHPs, and age of onset. Regarding need-service matching, many youths’ needs went unaddressed. Racial/ethnic minority youth were more likely to receive a mismatch relative to White youth. Older youth, females, Black,

Hispanic, ‘other’, and high-risk youths were more likely to have a mismatch while youth with

INT symptoms were more likely to have a match. These results indicate potential disparity in decision-making when allocating resources. Lastly, need-service matches were not associated with a global (‘any’) recidivism measure, which is dissimilar to past research. However, mismatches were related to violent reoffending. When broken down by service eligibility, findings were mixed, where matches sometimes resulted in greater recidivism, especially for

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youth who received mental health services, but was also associated with decreased reoffending, particularly for youth with substance use needs and who received AOD treatment.

Furthermore, and in line with past research, ACEs were prevalent amongst the present sample. Although ACEs were related to all recidivism types for males, they were only associated with violent reoffending for females. Additionally, only one program type, AOD treatment, affected the relationship between ACEs and recidivism. This effect was significant for only males.

Mental health symptoms differentially affected the relationship between gender and recidivism, potentially offering solutions for gender-responsive interventions. Additionally,

AOD treatment was again the only intervention that had a significant impact on recidivism, and this effect was only evident for INT and co-occurring symptoms. Several youth attributes, including gender, age, race/ethnicity, risk class, and current substance use, were frequently associated with different recidivism outcomes for youth in the INT, EXT, and co-occurring subsets. Finally, both ACEs and MHPs were associated with onset of deviant behavior. Similar to past research, age of onset was associated to all recidivism types, wherein youth with a later onset of deviance evidenced decreased odds of reoffending. Chapter Five will discuss the results in greater depth, including their limitations and avenues for future research. Moreover, several policy implications can be identified following from these findings and are outlined in the following chapter.

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CHAPTER FIVE DISCUSSION Numerous studies have identified the deleterious effects of need-service mismatching

(Andrews & Dowden, 2006; Andrews et al., 1990; Baglivio et al., 2018; Dowden & Andrews,

2004; Peterson-Badali et al., 2015; Vieira et al., 2009; Vitopoulos et al., 2012), ACEs (Baglivio et al., 2014, 2015; Barrett et al., 2014a, 2014b; Dembo et al., 1993, 1995; Fox et al., 2015;

Maxfield & Widom, 1996; Teague et al., 2008; Vaughn et al., 2017; Wolff & Baglivio, 2017;

Wolff et al., 2017), and mental health concerns (Barkley, 1996; Copeland et al., 2007; Craig et al., 2019; El Sayed et al., 2016; Hoeve et al., 2013, 2015; Okzan et al., 2018; Teplin et al., 2002) have on recidivism outcomes for justice-involved youth. Although many studies have assessed the effect of need-service matching, as well as the impact of ACEs and MHPs, on youth recidivism independently, the current study sought to explore the relationships amongst these factors and how they contribute to reoffending. Furthermore, as prior literature has typically focused on EXT disorders (Achenbach, 1985; Achenbach & Edelbrock, 1981; Robins, 1996), differences across INT, EXT, and co-occurring symptoms were investigated to ascertain their differential impact on youth recidivism. Life course theory was also tested through an exploration of differences between early- and late-onset youths.

Current practice indicates that both trauma and mental health issues are not criminogenic needs that should be prioritized by correctional interventions (Bonta & Andrews, 2017).

However, the present work sought to answer the question of whether trauma (via ACEs) and

MHPs are substantial needs that should be addressed within the juvenile justice system.

Moreover, the effect of current programs utilized in Washington State’s juvenile justice system was assessed to test whether they help decreased recidivism risk for youths with ACEs or MHPs,

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which may have implications for ACEs and MHPs as responsivity factors. Essentially, this work sought to identify the prevalence of both ACEs and MHPs in a sample of Washington State youth on probation or community supervision and whether current interventions are effective for justice-involved youths with documented trauma histories or mental health issues. The findings presented above offer insight into service provision within the juvenile justice system in

Washington State, particularly for youth with ACEs and/or MHPs.

Study Findings

The research findings presented in the preceding chapter provide a picture for what need- service (mis)matching and provision of services for youths with documented ACEs and/or MHPs looks like for youth on community supervision in Washington State. Results from the current study were sometimes anticipated but other times unexpected when considering what past literature has shown. Overall, the findings point to areas within the juvenile justice system that should be researched further to better provide resources to youths with trauma and mental health needs, which may serve to improve public safety and youths’ personal well-being.

Need-Service Matching

The first research question concerned disparity in need-service matching in addition to the efficacy of matching in reducing recidivism risk. Generally, many youths’ identified needs were not addressed by programming, as the majority of youth were classified as a need-service

‘mismatch’. The following paragraphs further discuss disparity in need-service assignment and differences in recidivism between youth with matches and mismatches.

Disparity within need-service matching. The findings demonstrated that racial/ethnic minority youth were disproportionately represented in the need-service mismatch group. This result aligns with past research; for instance, Rawal and colleagues (2004) found that

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racial/ethnic minorities in their sample were underserved, particularly for mental health needs.

Again, these findings could be due to how a need-service match or mismatch was defined.

To further explore potential disparity in need-service matching, other youth characteristics were examined as predictors of need-service group. The results revealed that several variables, including age, gender, race/ethnicity, risk category, and INT symptoms, were associated with service assignment. It was expected that risk class would be related to need- service matches, as the RNR framework articulates the importance of providing higher risk individuals with more resources (Bonta & Andrews, 2017). However, the study findings revealed that high-risk youth were less likely to receive a need-service match, which runs counter to the

RNR perspective and other research. For example, Nelson and Vincent (2018) found that low- risk youth were less likely to receive need-service matches. Findings pertaining to race/ethnicity were also anticipated, as other studies have provided evidence for disparity in need-service matching for between White and racial/ethnic youth (Rawal et al., 2004).

Age was not expected to be a significant predictor of need-service matching, but it may be the case that it was related due to risk associated with age of onset, where youth with an earlier age of onset are deemed more at risk for continued deviance (Bonta & Andrews, 2017;

Moffitt, 1993). Consequently, it could be recognition on the part of juvenile justice actors that younger youth are in more need of services to attempt to prevent their continued engagement in deviant behavior. Moreover, females might have been less likely to receive need-service matches as a result of under-identification of gender-specific needs. As discussed previously, the feminist pathways literature has identified victimization experiences, MHPs, and substance use concerns as risks that are common in female offenders (Belknap & Holsinger, 2006; Broidy et al., 2018;

DeHart & Moran, 2015; Gehring, 2018). Although mental health and substance use needs were

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measured, victimization experiences were not included as a need to be addressed by treatment. It may be possible that inclusion of trauma, or victimization more generally, as a need addressed by treatment would translate into more girls receiving need-service matches.

Moreover, it was unexpected that INT symptoms were associated with increased odds of a need-service match. On the contrary, one might anticipate that youth with EXT or co-occurring symptoms would be more likely to receive need-service matches as a result of their greater perceived risk resulting from more interpersonal (‘acting-out’, Fritsch & Burkhead, 1981) behavior or simply because their deviance is more observable (Links et al., 1998). However, one explanation for greater need-service matching for youth with INT symptoms is their history of depressive symptoms, which might have been viewed as a risk for the youth’s own safety (e.g., suicidal behavior or self-mutilation). Accordingly, these youth may have been more likely to receive mental health services matching their mental health need. It is also possible that certain youth characteristics resulted in a greater likelihood due to resource constraints. Although not examined in the present study, some counties have fewer programming resources than others, and if racial/ethnic or age demographics vary by county, it could be the case that certain counties are characterized by more or less equal need-service matching than others. Hence, a county-level analysis might have resulted in different findings.

Need-service (mis)matching and recidivism. Prior literature has indicated that need- service mismatches resulted in greater recidivism than matches (Baglivio et al., 2018; Luong &

Wormith, 2011; Peterson-Badali et al., 2015; Vieira et al., 2009). However, in the current study and with a balanced sample, youth with matches displayed the greatest rate of violent reoffending. The two groups did not differ on ‘any’ and non-violent recidivism. The findings became more complex once individual need-program matches were examined, wherein the type

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of program mismatch affected whether youth with mismatches or matches had higher recidivism likelihood (see Table 10).

These findings were unexpected, insofar that it was anticipated that need-service mismatches would result in the greatest rate of all reoffending outcomes. These results may be an artifact of the need-service match measure, which was biased towards identifying mismatches, as is discussed in the limitations section. Additionally, measurement of a substance use need was limited, as it pertained to only alcohol and drug use, rather than abuse. It is also important to note that program start, instead of completion, was utilized to determine whether youths received services in line with their needs. Consequently, a more stringent operationalization of ‘matches’ and ‘mismatches’ may have led to different findings.

There are several other possible explanations for these findings. It could simply be that some programs are not overly effective. For instance, youth identified to have an aggression need and who received ART, constituting a need-service match, may have had heightened recidivism due to ART being ineffective. This supposition is somewhat supported by other results from the current study showing that only AOD treatment was related to decreased recidivism.

However, a more person-level explanation involves youth motivation, wherein youth may receive treatment but are simply not motivated to change (Haqanee et al., 2015). It could also be that other systems impacted a given youth’s success following services, including family problems (e.g., parental mental health and/or substance use problems) and community issues

(e.g., unsafe neighborhoods; Haqanee et al., 2015; Peterson-Badali et al., 2015). The ambiguous nature of these findings emphasizes the importance of further examination of youth need-service matching within the context of multiple levels (e.g., individual, family, peers, school, community) rather than just within the juvenile justice system.

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Prevalence and Impact of ACEs

The second research question concerned the effect of ACEs on youth reoffending as well as whether programming was effective in reducing recidivism for youth with ACEs. The prevalence of ACEs was also explored, as research in other states has indicated that these experiences are frequent amongst justice-involved youths (Baglivio et al., 2014; Burke et al.,

2011; Cronholm et al., 2015; Flaherty et al., 2009). In line with this research, the findings demonstrated that ACEs, particularly parental separation or divorce, family violence, and household incarceration, were common. Compared to males, females reported higher incidences of all ACEs. The only substantive difference between males and females in ACE indications was sexual abuse, where females were nearly three times more likely to report such a history. Despite indicating more exposure to ACEs, female youth typically do not commit as serious of offenses as males and are less likely to recidivate with serious offense compared to males (Bonta &

Andrews, 2017). Accordingly, the higher prevalence of ACEs amongst females may not result in a stronger relationship between these experiences and recidivism for this group when compared to males simply because they are less likely to reoffend in general. Further differences concerning ACEs and MHPs, as well as ACEs and recidivism, were explored.

ACEs and MHPs. Findings revealed that youth with co-occurring symptoms typically reported more ACE exposure than either youth with INT or EXT symptoms. These results were not unexpected, as individuals with co-occurring disorders often report more deleterious outcomes than those with either INT or EXT disorders (Copeland et al., 2007; Hoeve et al.,

2013, 2015). These results appear to generalize to ACEs; yet, it is important to note that it was not possible to identify whether ACEs preceded MHPs or not in the dataset used9. Additional

9 This data constraint is discussed further in the limitations section.

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research that can establish the temporal ordering of traumatic experiences and onset of MHPs may find that ACEs are not typically more common amongst youth with co-occurring symptoms.

Notably, emotional abuse and household substance abuse were reported more frequently by youths with INT symptoms; hence, specific experiences may differentially contribute to manifestation of MHPs. For instance, it was not unexpected that emotional abuse and INT symptoms were related, as past research has demonstrated a relationship between these experiences (Bifulco, Moran, Baines, Bunn, & Stanford, 2002; Powers, Ressler, & Bradley,

2009). Further research regarding the effect of individual ACEs on MHPs may help illuminate other differences across multiple mental health concerns.

ACEs and recidivism. Moreover, higher ACE scores were related to increased odds of

‘any’, violent, and non-violent recidivism for the total and male samples but only violent reoffending for females. The relationship between ACEs and violent recidivism is supported by past research, as other researchers have documented an association between serious, violent offending and higher ACE scores (Fox et al., 2015; Perez et al., 2018). Regarding the association between ACEs and non-violent recidivism, it may be that substance use played a role, as non- violent recidivism included property reoffending. Although not shown in the above results, additional analyses revealed that substance use was related to higher ACE scores, and it is possible that youth committed non-violent crime to support their alcohol and/or drug habits.

Qualitative research has identified drug use as a coping mechanism for females who have been exposed to trauma, and these women resorted to property crime to support their substance use

(DeHart, Lynch, Belknap, Dass-Brailsford, & Green, 2014; Gilfus, 1993). Alternatively, the relationship to non-violent reoffending could be due to continued use of substances, as other researchers have found that ACEs and substance use are related (Widom & White, 1997).

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ACEs and gender. The findings provide partial support for the differing effect of trauma on recidivism across gender. ACEs may have been more predictive for male recidivism simply because males are more likely to recidivate (Bonta & Andrews, 2017). In contrast, females reported higher exposure to each ACE measure, and the lack of an effect of ACEs on female reoffending may be an artifact of ACEs being so prevalent amongst these girls. Stated otherwise, the pervasiveness of ACEs amongst these girls may obscure the effect these experiences have on recidivism. A quasi-experimental study utilizing matching and that compares a sample of females with no or few (1-3) ACEs to those with several (4 or more) could provide more information on the relationship between ACEs and recidivism for justice-involved females.

Alternatively, differences found for gender may be due to how males and females differentially respond to trauma exposure. While males are more likely to engage in violent behavior following trauma (Baglivio et al., 2014; Chiu et al., 2011), females tend to exhibit INT symptoms (Baglivio et al., 2014; Leadbeater et al., 1999). As such, ACEs may have demonstrated a stronger predictive relationship for male reoffending as crime is often interpersonal. Additionally, and as suggested by the feminist pathways literature, ACEs are possibly a stronger predictor for initial contact with the juvenile justice system, rather than continued involvement with it (e.g., recidivism) due to maladaptive coping methods utilized to address reactions to trauma exposure (Broidy et al., 2018). Hence, measurement of point of contact with the juvenile justice system, instead of just recidivism, may yield significant results for female youth.

Furthermore, although there was no significant relationship between ACEs and reoffending for girls, this finding does not necessarily indicate that they do not require an intervention or other form of support. Depending on their specific needs and/or traumatic

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reactions, services may best be provided outside of the juvenile justice system by other social institutions, particularly as females have a lesser propensity for deviance compared to males. In other words, the juvenile justice system should not be used to treat girls if they can better be served outside of it, as the negative environment of the juvenile justice system may actually serve to punish these girls further and potentially inflict even more trauma.

ACEs and programming. The effect of programming on the path between ACEs and recidivism was also examined. Recall that only AOD treatment was associated with reduced recidivism and that AOD treatment mediated the relationship between ACEs and recidivism for the total and male samples. Results concerning FFT, FIT/MST, and ART were not entirely unexpected. Past research has shown a lack of an interaction effect between family-based programing and ACEs, as well as between ART and ACEs (Kowalski, 2018).

Mediated path analyses were conducted with these programs as mediators, and none of the indirect effects were significant. This lack of a treatment effect may be due to several factors, including insufficient resources, improperly trained staff, program drift, or lack of youth motivation. The current study was not able to examine these other possibilities, but additional research may be able to better identify why most programs examined in the present study did not affect the relationship between ACEs and recidivism. Furthermore, whether youths’ needs were matched with services was not included in these analyses, and lack of matching may partially explain why certain programs did not appear to be effective for youths with ACEs.

Effect of MHPs on Gender and Recidivism

The third research question concerned the mediating effect of multiple MHPs on the relationship between gender and reoffending. Past literature has documented gender differences in prevalence of INT and EXT symptoms, wherein females are more likely to display INT

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behaviors (Espinosa et al., 2013; Kashani et al., 1987) while males more often perpetrate EXT behaviors (Espinosa et al., 2013; Kazdin, 2005). Following from this literature, analyses were conducted to examine the differential impact of INT, EXT, and co-occurring symptoms on the relationship between gender and recidivism. Findings demonstrated that the indirect effects for the INT, EXT, and co-occurring mediation models were significant. While females were more likely to have INT symptoms, males had a higher likelihood of EXT and co-occurring symptoms, which aligns with past research.

Moreover, youth with INT symptoms exhibited decreased likelihood of reoffending, but youth with EXT and co-occurring symptoms demonstrated a greater likelihood. These results appear to suggest that INT symptoms may be a protective factor, especially for females, in reducing recidivism. This result does not necessarily indicate that no negative outcomes follow from INT symptoms; rather, it could simply be that youths with these symptoms are more likely to harm themselves or engage in ‘acting-in’ behavior (Fritsch & Burkhead, 1981). Consequently, it would make sense that their recidivism is lower, as crime involves acting against another person or property. Conversely, EXT symptoms may thus be a risk factor for continued deviance, particularly for males, as a result of ‘acting-out’ (Fritsch & Burkhead, 1981) behavior.

Past research has also shown that people with co-occurring disorders have worse outcomes than those with either INT or EXT disorders (Copeland et al., 2007; Hoeve et al., 2013, 2015); therefore, the finding that co-occurring symptoms mediated the relationship between gender and recidivism was not unanticipated. Similar to EXT symptoms, it seems co-occurring symptoms may act as a risk factor, especially for males, when considering youth recidivism.

MHPs, Programming and Recidivism

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The fourth research question concerned the mediating role of programming in reducing the effect of MHPs on youth reoffending. The findings demonstrated that AOD treatment mediated the relationship between INT symptoms and recidivism. Specifically, the results suggest that AOD treatment is particularly effective for youth with INT symptoms, and such symptoms may be a responsivity factor in and of themselves. As discussed previously, it may be the case that youth with INT symptoms are simply less likely to engage in criminal behavior due to the ‘acting-in’ nature (Fritsch & Burkhead, 1981) of their symptoms. However, it is also possible that these youth are simply more responsive to AOD treatment than other youths. This finding is interesting, as other researchers have indicated that depression is an individual responsivity factor that may hinder offenders’ success following treatment (Bonta, 1995; Van

Voorhis, 1997). Yet, Hubbard and Pealer (2009) discovered that depression contributed to an improvement in cognitive distortions following cognitive-behavioral treatment in a sample of

257 adult men in a community correctional center, concluding that treatment was more effective for depressed men than it was for those who were not depressed. The findings presented here seem to indicate that INT symptoms are a responsivity characteristic to be considered in youth correctional programming and require further exploration.

Additionally, AOD treatment did not mediate the relationship between EXT symptoms and recidivism. Lack of a significant relationship may be a result of the EXT measure, which included only an ADHD diagnosis. Inclusion of other EXT disorders may change the results.

Conversely, AOD treatment mediated the relationship between co-occurring symptoms and reoffending. Further, co-occurring symptoms were associated with an increased recidivism likelihood. These findings indicate that co-occurring symptoms are a possible risk for recidivism, especially for males, but AOD treatment may help ameliorate this effect. It is possible that the

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INT symptoms present within the co-occurring measure resulted in youth being more responsive to AOD treatment, but further work is needed to clarify how large of an effect this treatment may have for youth with such symptoms as well as whether INT or EXT symptoms are the driving force behind potential change. As mentioned, it appears the former likely contributes more to responsivity than the latter based on analyses done with INT- and EXT-only measures.

Youth attributes predictive of recidivism for mental health subsets. The sample was also partitioned into youth with INT, EXT, or co-occurring symptoms to explore whether youth characteristics differentially predicted recidivism across the three groups. ACEs were associated with increased ‘any’ recidivism for youth with INT symptoms as well as violent reoffending for youth with INT and EXT symptoms. Lack of significant findings for the relationship between

ACEs and recidivism for youth with co-occurring symptoms was unexpected. Similar to ACEs and female recidivism, it may be that the high prevalence of ACEs amongst these youth obfuscates the impact these experiences have on reoffending. It is further possible that the co- occurring measure, which included only symptoms and not diagnoses, resulted in non-significant results. More work is necessary to identify whether specific ACEs have a stronger relationship with recidivism across the mental health subsets, which may be driving the significant relationships between ACEs and recidivism for youth with INT and EXT symptoms.

It is also important to note that these analyses did not consider need-service matching.

Accordingly, it may be that a lack of matching regarding mental health needs and appropriate services could have contributed to youth with EXT or co-occurring symptoms having an increased likelihood of reoffending. Alternatively, placement of these youth in other services

(e.g., ART) may have also served to increase their likelihood of recidivism, as they may have been exposed to more deviant and/or aggressive peers. Past research has demonstrated that

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exposure to higher risk peers (Dishion & Tipsord, 2011; Gatti et al., 2009), particularly if youth have EXT symptoms (Dishion & Racer, 2013), may increase recidivism risk. In short, controlling for need-service matches for each type of service, as well as youth eligible for services, may better illustrate the effect of MHPs on recidivism and how programs affect the relationship between MHPs and continued deviance.

Associations between ACEs, MHPs and Age of Onset

The final research question concerned the relationships between ACEs, MHPs, onset of deviance in addition to the effect of age of onset on recidivism. Analyses addressing age of onset revealed ACEs, EXT symptoms, and co-occurring symptoms were related to a greater likelihood of an early age of onset. In contrast, INT symptoms were associated with a later onset. Once other controls were included, ACEs were no longer associated with onset of deviance. These findings are supported by past research, insofar that other studies have documented a relationship between ACEs and an early age of onset (Baglivio et al., 2015; Fox et al., 2015) and certain

MHPs and early onset (Ruchkin et al., 2003).

Furthermore, and as with previous analyses, INT symptoms appear to be a protective factor, and past research has demonstrated a relationship between INT behaviors and a later age of onset (Aguilar et al., 2000). Again, the effect of INT symptoms may have to do with the

‘acting-in’ nature (Fritsch & Burkhead, 1981) associated with these concerns. Thus, youth with

INT symptoms may be more likely to cause harm to themselves but typically do not have any interaction with the juvenile justice system until they are older. Other research has shown a relationship between INT disorders (e.g., depression) and violent behavior (Fazel et al., 2015;

Okzan et al., 2018; Posick et al., 2013), but it may be the case that such youth take longer to perpetrate interpersonal aggression compared to youth with EXT or co-occurring symptoms.

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Additionally, some life-course theorists argue that an early age of onset is associated with EXT behaviors (Moffitt, 1993). Furthermore, it may be that INT are related to a later age of onset because females are more likely to be diagnosed with INT disorders (Espinosa et al., 2013;

Kashani et al., 1987) and have a later age of onset (Block, Blokland, van der Werff, van Os, &

Nieuwbeerta, 2010). Therefore, it may be gender, rather than INT symptoms themselves, that are driving the relationship between INT symptoms and age of onset. More research is needed to better understand why youth with INT symptoms display a later age of onset.

Moreover, an early age of onset was associated with an increased likelihood of each recidivism outcome. This finding was expected, as the life-course literature has noted the relationship between early onset of deviance and persistent offending (Farrington, 2006; Hoeve et al., 2008; Moffitt, 1993, 2006; Sampson & Laub, 1990, 1993). Although the relationship between age of onset and recidivism has been documented in the past, the current work demonstrates this finding holds across multiple types of recidivism. These findings have relevance for correctional interventions and might inform allocation of resources. If it is the case that late onset youth are less likely to recidivate, then more resources should be provided to early onset youth, who may also have more severe needs. For instance, the findings demonstrated that these youth reported higher ACE scores and were more likely to have EXT and co-occurring symptoms, all of which were shown to be related to increased recidivism in the current study and in other studies (Baglivio et al., 2014, 2015; Barrett et al., 2014a, 2014b; Copeland et al., 2007;

Hoeve et al., 2013, 2015; Wolff & Baglivio, 2017). Many of these findings shed light on work that still needs to be completed, but it is also important to note some of the limitations of the present study, as is done in the following section.

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Limitations and Avenues for Future Research

The current work is not without limitations, and it should be noted that these results are exploratory. However, one key limitation this study concerns the sample, as only probation youth or those on community supervision were included. Hence, external validity may be a concern, and the results may not be applicable to incarcerated youth. Along a similar vein, the above findings pertain to only one state. However, future scholars could expand this work to include incarcerated youth or even youth with a history of deviance who have been placed in correctional mental health facilities. Moreover, the tests conducted hitherto could be performed in other states, as many other states utilize the PACT and keep track of youth programming.

Furthermore, the PACT instrument has been used in Washington State for several years; thus, it may be unclear whether the findings presented represent program drift regarding office policies, protocols, procedures, and/or training on the instrument to inform case management

(Nelson & Vincent, 2018). Program integrity, or adherence to how the PACT is supposed to be administered, is particularly essential in this study, as programming eligibility is based off PACT responses. Consequently, improper administration of the assessment, or failure to adhere to case management recommendations derived from youths’ responses (e.g., designation of risk level classification), may in turn affect the programming youth receive.

Future work could address these concerns by pairing the above analyses with Lipsey’s

(2009) Standardized Program Evaluation Protocol (SPEP) to better address whether provision of services found in the PACT align with SPEP recommendations – targeting high risk youth, adherence to the treatment model via sufficiently trained staff, providing an appropriate dosage of an intervention, and supplying the appropriate service to youths. The current work addresses this final point through the exploration of need-service matching. Yet, further analyses remain to

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be performed to examine differences between low, moderate, and high-risk youth who received need-service matches, as well as incorporation of a measure identifying how much treatment youths received. Baglivio and colleagues (2018) have examined the effect of treatment dosage in justice-involved youth who received services and found that matching services to youths’ specific needs, while also providing an appropriate dosage of treatment, contributed to a reduction in youths’ risk to recidivate.

The way in which a need-service match was operationalized in the present study may be limited, as it is not necessarily feasible to address all of a given youth’s needs during one probation disposition. Additionally, because the RNR framework emphasizes the importance of allocating intervention resources to higher risk youths (Bonta & Andrews, 2017), the criminogenic needs of lower risk youth may not be addressed. Accordingly, prioritization of needs based on severity, as well as the total number of services received by youth in a given disposition and development of a case plan that is reasonable for youths and their families, are important to consider when exploring need-service matching.

Moreover, identification of needs in the current study did not account for how recent a dynamic need occurred. For instance, and as described by Nelson and Vincent (2018), youths with historical substance use may currently be sober and may not require an intervention. Yet, such youth may be identified as having a substance use need and may receive substance use programming as a result even though the need is not truly present. In other words, certain needs may be overestimated without greater context surrounding the youth’s historical and current behavior. This limitation could be addressed via a longitudinal analysis of needs and how they change over time. Such an examination could also assess changes in need-service matching across youth PACT reassessments.

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Additionally, all need types were treated equally in this study, and this conceptualization of a need-service fit does not directly correspond to the RNR framework. Rather, certain risk factors (e.g., the Big Four) are considered stronger predictors of recidivism than others (Bonta &

Andrews, 2017). As such, grouping needs may mask variations regarding the effect of need- service combinations (Lattimore et al., 2012). This limitation could be addressed by future research, in which need-service matches and mismatches could be examined individually. For instance, Gill and Wilson (2017) investigated seven service-need dyads (e.g., substance abuse, education, employment, anger management, attitude change, and mentoring), where each dyad was coded as having a poor (e.g., service not received when it was needed or service not needed but received) or good fit (service needed and received or no service needed/received) for individuals returning to the community from the correctional system. They then created a service-need fit index ranging from 0 to 7 points. Their results demonstrated that service-need fits were associated with reduced reoffending. Such a need-service scheme could similarly be used to identify specific need-program matches with youths who receive the PACT assessment and could better identify particular (mis)matches that are associated with youth delinquency.

A further limitation to the need-service matching portion of this study concerns decision- making. Probation officers, or other case management personnel, may be required by a court order to provide youth with a specific intervention as part of their probation (Nelson & Vincent,

2018). Stated otherwise, decision-makers (e.g., judges) who possibly are not aware of the RNA results, may play a role in intervention provision. Accordingly, and as advocated by Nelson and

Vincent (2018), use of RNA case planning should including training for judges and attorneys so they better understand RNAs and the RNR framework and have buy-in for the RNA process.

Further research in Washington could involve interviews or survey work with such decision-

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makers to ascertain their understanding of the PACT, their agreement with the instruments, and potential factors that contribute to their decision to assign an intervention to a given youth.

There are also notable limitations involving the ACEs construct. Examination of ACEs does not account for frequency of experiences or how recent exposure to the trauma occurred.

This may be problematic, as people are generally more influenced by more recent events compared to distal ones, also termed the recency effect (Maughan & Rutter, 1997).

Consequently, youth who have more recent exposure to ACEs may be more affected by those events than youth with a more distant history of trauma.

The limitations of ACEs, as conceptualized in the current study, also have theoretical considerations, particularly for general strain theory. As articulated by Agnew (1992), the duration of a strain is essential, wherein repeated or chronic strain increases the probability of a maladaptive response. Additionally, more recent strains have a larger effect than those that occurred longer ago. For instance, Farrell and Zimmerman (2018) investigated the cross- sectional, short-term, and long-term effects of exposure to violence, substance use, property offending, and violent crime. The authors found that perpetration of violent offending, but not substance use or property crimes, following distal experiences with violence persisted when statistically controlling for recent exposure to violence. However, the long-term outcomes of violence exposure were not as strong as short-term outcomes, indicating that the most severe outcomes following exposure to violence are immediate. Farrell and Zimmerman (2018) concluded that controlling for recent violence exposure is critical when assessing the short-term or long-term consequences of experiences with violence. Better identification of when ACEs occurred could thus improve upon the present work.

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Moreover, the ‘yes’ or ‘no’ nature of the ACEs measures does not allow for weighting experiences by the frequency, duration, or intensity in which they were experienced. Scholars in the child maltreatment field deem individual specification necessary to better account for the differential impact of certain maltreatment experiences over others (Smith & Thornberry, 1995).

A further concern involves the narrowness of the ACEs scale, which has been critiqued by other scholars. For instance, Finkelhor, Shattuck, Turner, and Hamby (2013) have suggested that peer rejection, low socioeconomic status, and witnessing violence outside of the home should also be included. Consequently, future research could examine these issues and assess whether frequency, duration, intensity, and/or other types of experiences affect youth deviance.

Many offenders are also victims of crime (Deadman & MacDonald, 2004), and this group of victim-offenders may differ from victims-only and offenders-only in their risk and protective factors (Engström, 2018; TenEyck & Barnes, 2018). In terms of ACEs, one might expect that youth who have been victims (e.g., abuse) may differ from other youth who report non- victimization ACEs (e.g., parental substance use). It may also be that certain experiences could help expand the ACEs construct and help differentiate between victim-offenders and offenders- only. For instance, association with deviant peers has been related to greater offending risk

(Weerman & Hoeve, 2012) and victimization (Averdijk & Bernasco, 2015). Accordingly, future research could address victimization differences within the ACEs construct, as measured here and in other studies, to ascertain whether the type of ACE affects youths’ outcomes and whether other experiences, including peer deviance, may contribute to the ACEs construct. Future research could also explore this limitation by examining differences between victim-offenders and offenders-only on recidivism, mental health, and substance use outcomes. This limitation

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may also be addressed through latent variable analyses to determine whether additional youth experiences load onto one construct.

In regard to MHPs, the measures of INT, EXT, and co-occurring symptoms are limited.

The PACT in Washington includes information on depressive symptoms and somatic complaints, but these are neither diagnoses nor inclusive of all types of INT symptoms.

Similarly, only an ADHD diagnosis was included in the EXT measure. As described above, other disorders qualify as EXT (e.g., ODD, CD, SUD, and ASPD). The co-occurring variable is further limited, as it is a combination of both the INT and EXT measures. Future work could improve upon these concerns by including verified mental health diagnoses and expanding on the diagnoses included. Likewise, the measure of substance use utilized in the current study represented consumption, rather than addiction. A clearer measure of addiction, rather than misuse or abuse, could also be included in the EXT measure.

An issue relevant to the relationships amongst ACEs, MHPs, and delinquency pertains to temporal ordering. It was not feasible, nor would it be ethical, to conduct a true experiment with these particular topics. Resultingly, it cannot conclusively be determined whether there is a causal relationship amongst these measures or what the causal direction is. As an example, it could be the case that ACEs predated MHPs or vice versa. It is also possible that previous involvement in the juvenile justice system contributed to onset of an MHP. Some of these problems can be addressed, such as inclusion of prior justice-system involvement as a control variable. Other steps could be taken, such as PSW or other types of statistical matching, as was done with analyses involving need-service matching. Once better measures of MHPs are included, it would also be advisable to perform weighting or matching across the groups to better explore differences in reoffending across INT, EXT, and co-occurring symptoms.

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Future research could also include a longitudinal analysis to investigate the long-term effect of ACEs and whether associated substance use, MHPs, and deviant behavior persist. For instance, Kjellstrand et al. (2018) examined the effect of parental incarceration on children’s

EXT behaviors over time via latent growth curves and found that parental incarceration was related to growth in EXT behaviors across adolescence but not baseline levels of such behavior.

A latent growth curve approach may be particularly useful, as such models aid examinations of change in behavior over time. Moreover, protective factors were not examined at length in the present study. Further exploration is required to identify potential protective factors that may help reduce the effect of ACES and/or MHPs on youth reoffending. As an example, it would be possible to examine the moderating effect of social support via PACT responses on MHPs for youth who have experienced ACEs and those who have not. Past research has indicated that social support may help decrease the negative effect of certain adverse experiences (e.g., family imprisonment; Lösel, Pugh, Markson, Souza, & Lanskey, 2012; Murray et al., 2012) on mental health outcomes. Essentially, social support can be a protective factor that helps negate the harmful effects of ACEs.

Moreover, further research should include control variables in the mediation analyses.

Only simple mediation models were tested in the current study, and as noted above, it is unclear whether gender or ACEs and MHPs, themselves, are driving some of the significant mediation indirect effects. Therefore, future work could incorporate more complex models that include several control variables, including gender, race/ethnicity, age, and risk class.

Additionally, results pertaining to the effect of programming require continued exploration. More information is required to identify whether programs were simply ineffective as a result of their treatment models or due to other considerations, such as insufficient resources,

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lack of program integrity, program drift, and/or insufficiently trained staff. For example, not all counties have the programs examined, and some may have limited space for youth. A multilevel model analysis that considers state- and county-level effects may help to illuminate whether the nonsignificant findings discussed above were a result of resource constraints.

Lastly, the analyses presented did not differentiate between violent and non-violent youth. Other researchers have demonstrated that violent, or extremely violent youth, may differentially respond to programming. For instance, Asscher, Deković, Van den Akker, Prins, and Van der Laan (2018) discovered differences between non-violent and extremely violent youth who participated in MST. Although both groups had a long-term positive change following MST, extremely violent youth had an initial increase in EXT behaviors in the first month of treatment that dissipated over time, indicating a need for long-term programming.

Therefore, future research could examine differences in program effectiveness for youth who have and have not perpetrated violence. Such research is particularly relevant, as violence does not necessarily decrease over time if it is not treated (Azur, Garraza, & Goldweber, 2011). In sum, future researchers should be strategic regarding how they conceptualize need-service matches, ACEs, and MHPs.

Policy Recommendations The present research lends itself to several policy recommendations that may help to improve both public safety and youths’ well-being. A central concern within the juvenile justice system pertains to justice-involved youths’ unmet needs (Luong & Wormith, 2011; Peterson-

Badali et al., 2015; Vieira et al., 2009). The current work addressed this issue through an exploration of need-service matching in addition to both ACEs and MHPs as possible needs that may affect youth recidivism. The following paragraphs include recommendations following from

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the current results that may help to inform practices for the youth probation and community supervision population in Washington State.

Need-service matching The above results indicated that need-service matching did not have a large impact on reducing youth recidivism. In fact, there were some instances where matching resulted in increased reoffending. However, it is not recommended that we give up on need-service matching, as other research has indicated that youth with matches are less likely to recidivate than those with matches. Rather, more research is required to clarify when need-service matching has a positive effect on youth recidivism and for which youth it is most beneficial. To facilitate this process, it would be important for the juvenile justice system to implement better need-service matching protocols, which start with appropriate use of RNAs, compliance with results, and program integrity. Generally, it would be important to identify ‘what works for whom’ (Baglivio et al., 2018) to shift from simply identifying risk to better meeting the specific needs and responsivity characteristics of youths to achieve rehabilitation.

Moreover, consideration of multiple, or cumulative risks and needs, may be necessary.

Interventions focusing on a single type of risk or need may be less effective. Justice-involved youth often experience several risks, and cumulative risk appears to have a particularly deleterious effect on youth outcomes (Loeber, Farrington, Stouthamer-Loeber, & White, 2008).

Accordingly, and as suggested by van der Put et al. (2012), it is imperative to measure youths’ progress across multiple domains while they receive programming, particularly in the attitude domain and other risk factors that have a relationship with youths’ attitudes. This type of case management is possible in Washington State, as youth are administered the PACT every six months while they are under supervision.

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Furthermore, early intervention is critical, as the importance of dynamic risk factors decreases as age increases, and interventions for older youth may thus be less effective if programs focus on dynamic risks (van der Put et al., 2012). Interventions should be age- appropriate, in that assessments should identify which criminogenic domains present are most likely to affect recidivism likelihood at varying ages but should also target non-criminogenic needs that may affect reoffending to optimize programming.

Additionally, practitioners should refrain from service provision for youths with no identified needs. Over-prescription of services, particularly for low-risk youth, may be a result of juvenile justice system practitioners wanting to provide something to youth under their supervision because they believe something is better than nothing (Fabelo, Arrigona, Thompson,

Clemens, & Marchbanks, 2015; Nelson & Vincent, 2018). However, provision of services, especially peer-oriented services, to low-risk youth may increase exposure to higher risk youth and result in a peer contagion effect (Dishion & Tipsord, 2011).

ACEs as a Need and Responsivity Factor

Traumatic experiences might constitute potential responsivity factors (Bates-Maves &

O’Sullivan, 2017). Accordingly, juvenile justice agencies may consider how a history of ACEs may impact youths’ success following treatment. One such avenue involves compiling victimization profiles that account for the cumulative effect of poly-victimization (Espelage,

Hong, & Mebane, 2016; Finkelhor et al., 2007). The present results indicate that cumulative experiences impact youth outcomes; accordingly, recognition of multiple experiences of trauma exposure may serve to help prevent further victimization, aid treatment, and facilitate healing

(Wolf & Prabhu, 2018). However, it may also be critical to first expand the ACEs construct.

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Other researchers have posited that more experiences, including low socioeconomic status, witnessing violence outside of the home, peer rejection (Finkelhor et al., 2013), association with deviant peers (Weerman & Hoeve, 2012), single-parent household (Wade, Shea,

Rubin, & Wood, 2014), and economic hardship, could be incorporated to better measure trauma.

Furthermore, structural forms of trauma, including poverty and racism, should be included to move beyond the abuse to prison pipeline (Saar et al., 2015) to the trauma to prison pipeline

(Baulme, 2018). Furthermore, Brennan et al. (2012) identified pathways to offending for adult females that included impoverishment and marginalization, which may suggest that structural forms of trauma have a substantial impact on female offending. A broader conceptualization of trauma may thus better account for the experiences of justice-involved females, as they are more likely to experience trauma, including racism and poverty, than males. As described by Baulme

(2018), consideration of both interpersonal (e.g., abuse) and structural (e.g., poverty and racism) may better elucidate why girls, particularly those of color or who are impoverished, end up in the juvenile justice system.

Consequently, care should be taken to identify whether youth have a trauma history, as certain behaviors may be due to trauma reactions, which are adaptations to help individuals cope with ongoing or historical trauma (Center for Substance Abuse Treatment, 2014). Such reactions may be misdiagnosed as MHPs, particularly for underserved populations (e.g., racial/ethnic and/or impoverished groups; D’Andrea, Ford, Stolbach, Spinazzola, & van der Kolk, 2012). As a result, youth with trauma histories, and resulting trauma reactions, may be sent to the juvenile justice system and punished for their responses rather than receiving treatment to address their traumatic pasts (Chesney-Lind, 1989). It is thus recommended that more preventative efforts be made to halt such youth from reaching the juvenile justice system. For instance, screening could

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be done in the school or child welfare systems to identify youth have a history of trauma and are experiencing traumatic reactions.

Moreover, if a trauma history is detected, it would be important to address it over time in order to have a to long-lasting effect. For instance, Farrell and Zimmerman (2018) suggested that repeated interventions are necessary for individuals who have been exposed to violence, as they found that many of the subjects in their study reported multiple experiences with violence over time that resulted in acute (one year) and long-term (13 years) effects on subjects’ perpetration of violent offending. The authors also indicated that screening and documenting earlier experiences with violence, rather than just recent exposure, is essential when providing intervention services.

Interventions should not only address trauma experiences but also strengthen protective factors an individual can develop, including healthy coping skills. An example of one intervention involves trauma-focused cognitive behavioral therapy, or TF-CBT, which provides instruction on modulating affect, relaxation skills, and positive coping strategies for youths who have experienced violence and their parents (Cohen, Deblinger, Mannarino, & Steer, 2004).

Mental Health Issues as Needs and Responsivity Factors

Following from the results of the current study, it is recommended that interventions be adjusted to better address the specific needs and responsiveness of youths with EXT symptoms.

As indicated previously, AOD treatment was effective in mediating the relationship between reoffending and both INT and co-occurring symptoms. This result did not hold for youth with

EXT symptoms. Hence, it is advised that steps be taken to identify types of programming are effective for youth with EXT symptoms.

Additionally, it is recommended that more complete assessments be conducted with justice-involved youth prior to treatment interventions. Past research has found that offenders

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with many issues (e.g., history of sexual abuse, depression, low IQ and self-esteem, certain personality types) require different services than people with fewer concerns (Hubbard & Pealer,

2009). It is possible that youths with trauma exposure and MHPs also need different interventions. More specifically, it may be necessary to address these needs (ACEs and MHPs) prior to service delivery, including addressing trauma reactions (e.g., emotional dysregulation, self-harm, substance use; Center for Substance Abuse Treatment, 2014) or symptoms of MHPs that interfere with the youth being successful in desisting. Such youth may not be able to fully engage in treatment if these concerns are not first addressed. Indeed, multiple individual characteristics, or responsivity factors, have a cumulative effect on program success, where each increase in problematic issues is resulted to decreased likelihood of success (Hubbard & Pealer,

2009). Thus, screening prior to interventions could help identify and address responsivity factors that will hinder treatment success.

Moreover, it is advised that the PACT be validated for youth with MHPs. Currently, no study has addressed whether the items and domains present in the PACT accurately predict recidivism risk for youth with mental health issues. Such work may be necessary now, as a valid risk/needs instrument can better inform case management. For example, Olver and Kingston

(2018) posited that risk instruments that have acceptable discrimination and calibration are useful as part of the assessment process for justice-involved individuals who also have MHPs.

Specifically, inclusion of a risk instrument facilitates appropriate classification and interventions that are not solely focused on treatment of mental health concerns. Stated otherwise, a valid instrument may be more useful in identifying risk and non-mental health needs and can improve the success of people with MHPs.

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Life Course Theory and Interventions

The results indicated that early-onset youth had a higher recidivism risk. However, provision of services to youth earlier should not be given simply because they have a greater likelihood of reoffending. Rather, interventions should occur with the knowledge that such youth are more likely to have ACEs and/or MHPs, as demonstrated in the present study. Accordingly, services provided need to target specific needs to improve responsivity to programming.

Moreover, the findings showed that younger youth were more likely to receive a need- service match, so it is the case that staff recognize that younger youth are at a greater risk for continued deviance. Yet, there are also older youth that possess an early age of onset and are not receiving need-service matches as a result of their current age. Consequently, greater attention needs to be afforded to youths’ criminal history and when onset of deviance occurred. Stated otherwise, correctional staff are already providing greater services for youth with an earlier age of onset, but this should be an intentional decision (e.g., described in policy) that also includes older youth with an early age of onset.

Program Evaluations

The findings presented seem to indicate that many interventions simply do not work for justice-involved youth on community supervision in Washington State. These results are reminiscent of the ‘nothing works’ rhetoric following Martinson’s 1974 report. Yet, the analyses conducted in the present study are not sufficient to make this claim, and the results should not be interpreted as ‘mostly nothing works’. Rather, program evaluations of the interventions studied are advised. Several explanations can explain why many of the programs had no effect on youth recidivism, but the present study was not able to answer that question. Moreover, the results could be an artifact of inadequate adherence to the RNR model, which may explain non-

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significant results (MacKenzie, 2006). This is possible in the current study, as some low-risk youth received service they were not eligible for according to the program eligibility criteria.

Attention given to specific responsivity (e.g., attending to youths’ individual attributes) and need-service matching could thus improve recidivism outcomes. Program fidelity is included in the RNR framework (Bonta & Andrews, 2017); as such, lack of proper implementation or quality assurance may affect evaluations of different services, and it is unclear whether lack of adherence or the program itself explains unsuccessful services.

If program evaluations are conducted, it is important to differentiate across several subsets of youths in addition to exploring the effect of programming during and following the intervention. For instance, Asscher et al. (2018) assessed the effect of MST on a group of extremely violent youths and those who were not extremely violent. They utilized growth curves to explore within-treatment changes and found that the former group of youths had a different response to MST than the latter group. Non-linear change was evidenced, where extremely violent youth initially experienced deterioration in their EXT behaviors and relationship quality with parents but had a positive response after one month. In short, MST had a long-lasting effect for these youth. The current study utilized program start to identify whether youth participated in programming; consequently, change over time was not examined but could be to identify whether programming, as was the case in Asscher et al.’s (2018) study, is efficacious over time.

Additionally, it is recommended that the services examined in the present work be re- examined at different time points in youths’ programming participation (e.g., one-month in, halfway through, and following completion) to better assess the overall impact of these services on recidivism. Moreover, it would be worthwhile to explore differences across subsets of youth, including violent youth, youth with non-existent/less severe trauma history and those with more

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severe trauma histories (e.g. four or more ACEs), across mental health subsets (e.g., INT, EXT, and co-occurring disorders), males and females, White and racial/ethnic minority youth, and early onset and late onset youth. Results following from such analyses could inform service provision in Washington State for youth on probation or community supervision. Such specificity may also better identify ‘what works for whom’ (Baglivio et al., 2018).

Gender Responsivity

Findings from the current study demonstrated that many programs were not effective for youth in decreasing recidivism. However, AOD treatment, although significant in reducing reoffending risk for males and females, was non-significant in female models exploring the indirect relationship of this programing on the relationship between (a) ACEs and recidivism and

(b) co-occurring symptoms and reoffending. Conversely, these indirect effects were significant in the male models.

Policy recommendations specific to justice-involved females with trauma histories and advocated for by Saar et al. (2015) include lessening arrests and/or incarceration for females with past traumatization, reductions in the disparate enforcement of status offense violations on female youth; and attempt to decrease the number of girls in the foster care or child welfare system who cross over into the juvenile justice system. Additionally, trauma-informed programs could be implemented across the school, child welfare, mental health, and juvenile justice systems to address direct (e.g., abuse) and indirect (e.g., discrimination and poverty; Rich,

Bloom, Wilson, Corbin, & Rich, 2010) forms of trauma. Lack of a trauma-informed care approach may result in reactions to trauma being treated as deviant and punished rather than treated. Additionally, as traumatic experiences appear to be more prevalent in females, these recommendations serve to better address traumatic responses, thus potentially reducing

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criminalization of trauma reactions. Moreover, gender-specific programming could be necessary, where gender would be a responsivity factor.

Regarding MHPs and gender, INT symptoms appeared to be a responsivity factor generally and for females specifically. By contrast, programming did not seem to have any effect on females with EXT or co-occurring symptoms. Consequently, it is recommended that attempts be made to identify those with MHPs prior to implementation of programming. Additionally, it is advised that ACEs be considered as a responsivity factors for mental health treatment. For instance, as discussed by Willie, Kershaw, and Sullivan (2018), the efficacy of mental health programs can be improved by acknowledging ACEs as a contributor to females’ MHPs.

Generally, many of these recommendations trend towards an overarching theme – the importance of reconsidering ACEs and MHPs as needs and responsivity factors that will help improve success of justice-involved youth if properly identified and addressed.

Conclusion The current research sought to address the extent of need-service (mis)matching, as well as the impact of ACEs and MHPs on youth recidivism, within both a gender-responsive and life- course framework. The results demonstrated that need-service matching can help decrease reoffending if youth receive specified services but that several types of programming were not effective in recidivism reduction, even when youths have needs matching those services. ACEs were also prevalent in this sample and contributed to specific recidivism types. Additionally, such experiences were associated with MHPs, but programming typically did not mediate the relationship between ACEs and youth reoffending. The one exception was AOD treatment.

Moreover, findings indicated that INT, EXT, and co-occurring symptoms impact the relationship between gender and recidivism, wherein INT symptoms appeared to be a protective

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factor, especially for females. Conversely, EXT and co-occurring symptoms seemed to be risk factors, particularly for males. Although INT symptoms were associated with decreased reoffending, both EXT and co-occurring symptoms were related to a greater likelihood of recidivism. Furthermore, only AOD treatment affected the relationship between INT symptoms and any reoffending as well as between co-occurring symptoms and violent recidivism.

Additionally, results indicated that both ACEs and MHPs were associated with onset of deviance. ACEs, EXT, and co-occurring symptoms were related to an earlier age of onset while

INT symptoms were predictive of a later age of onset when no other youth characteristics were accounted. In turn, an early age of onset was associated with increased recidivism likelihood.

Finally, the results showed that most of the programs examined were not significantly related to recidivism. Overall, it is important to note that the results demonstrated several differences across youth subsets in terms of their ACEs, MHPs, and age of onset. It is important to note that the offending careers of individuals are not uniform (Broidy et al., 2018), whether they be male, female, White, a racial/ethnic minority, have a trauma and/or mental health history. Such variations necessitate individualized treatment.

Recommendations for future research and policies have been provided. The main theme of both involves further exploration of ACEs and MHPs as potential needs and responsivity factors that may serve to improve youth success following correctional interventions. Policy recommendations further highlighted why the current research is important for the juvenile justice field. Identifying needs that have traditionally been classified as non-criminogenic (Bonta

& Andrews, 2017), including ACEs and MHPs, prior to treatment implementation can help improve youths’ responsivity to various interventions. Responsiveness is particularly improved for justice-involved females, who typically indicate a higher prevalence of both ACEs and

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MHPs. Moreover, these results are relevant to multiple systems, including the educational, social work, child welfare, and mental health systems. Early identification of traumatic reactions and mental health concerns can foster a more preventative and rehabilitative approach so that youths with such experiences are not funneled into the juvenile justice system as a result of things that have happened to them. Stated differently, rather than punishing youth for trauma or mental health symptoms, we can help them heal.

The present work indicates that ACEs and MHPs are unmet needs in my sample of probation and community supervision youth in Washington State. More work is needed to identify how services can address the adverse experiences many of these youths have faced in addition to how INT, EXT, and co-occurring symptoms differentially impact youths’ responsivity to programming. Gender responsivity has been, and continues to be, addressed in the juvenile justice system. The current research emphasizes the importance of including trauma- and mental health-responsivity by specifically addressing the different ways youth with these concerns come to be involved in the juvenile justice system (e.g., trauma responses) and then providing interventions that effectively address their unique experiences by accounting for the impact traumatic responses and mental health symptoms have on youths’ participation in services. Attempts made to address these non-criminogenic needs that interfere with intervention success can improve upon current treatments and help develop new evidence-based practices that are effective for a wider range of justice-involved youths.

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Zigler, E., & Glick, M. (1986). A developmental approach to adult psychopathology. New York, NY: Wiley.

Zisner, A., & Beauchaine, T. P. (2016). Neural substrates of trait impulsivity, anhedonia, and irritability: Mechanisms of heterotypic comorbidity between externalizing disorders and unipolar depression. Development and Psychopathology, 28(4pt1), 1179-1210.

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APPENDIX A

PACT – FULL ASSESSMENT

209

210

211

212

213

214

215

216

217

218

219

APPENDIX B

SAMPLE DESCRIPTIVES FOR PACT RESPONSES

Total Males Females (n = 50,862) (n = 38,100) (n = 12,762) Item % M(SD) % M(SD) % M(SD) CRIMINAL HISTORY Age at first offense 2.70(1.05) 2.71(1.06) 2.64(1.01) Over 16 4.2 4.3 3.9 16 10.5 10.4 10.8 15 18.5 18.1 19.6 13 to 14 45.5 44.5 48.4 Under 13 21.4 22.8 17.4 Misdemeanor complaints 1.08(1.02) 1.08(1.03) 1.09(.97) None or one 37.8 38.7 35.0 Two 26.0 25.0 29.1 Three or four 26.2 25.7 27.9 Five or more 9.9 10.6 8.0 Felony complaints 1.48(1.69) 1.65(1.74) 0.96(1.40) None 46.9 41.9 61.6 One 37.8 40.1 30.8 Two 10.1 11.7 5.4 Three or more 5.3 6.3 2.2 Weapon complaints .10(.30) .13(.33) .04(.19) None 89.6 87.5 96.1 One or more 10.4 12.5 3.9 Against-person misdemeanor complaints .59(.75) .56(.74) .68(.78) None 56.9 58.8 51.2 One 27.0 26.2 29.3 Two or more 16.1 15.0 19.5 Against-person felony complaints .38(.81) .42(.84) .27(.71) None 81.4 79.7 86.6 One or two 18.1 19.8 13.1 Three or more 0.5 0.5 0.3 Sexual misconduct misdemeanor complaints .02(.17) .03(.18) .01(.11) None 97.7 97.5 99.2 One 1.8 2.2 0.6 Two or more 0.3 0.3 0.2 Felony sex offense referrals .04(.20) .05(.23) .01(.08) None 96.5 95.5 99.4 One 3.3 4.2 0.5 Two or more 0.3 0.3 0.0 Number of times served at least 24hrs in detention 1.41(1.10) 1.42(1.10) 1.37(1.09) None 23.5 23.1 24.7 One 36.5 36.4 36.6 Two 15.6 15.5 16.0 Three or more 24.4 25.0 22.7 Number of times served at least 24hrs confined in JRA .19(.71) .21(.74) .14(.61) None 92.3 91.5 94.6 One 5.8 6.4 3.9 Two or more 1.9 2.1 1.5 Escapes .02(.13) .02(.13) .02(.14) None 98.5 98.6 98.4 One 1.4 1.3 1.5 Two or more 0.1 0.1 0.1 Failure to appear in court warrants .53(.79) .51(.78) .62(.83) None 65.8 67.5 60.7 One 15.1 14.5 16.9 Two or more 19.1 18.0 22.4 SCHOOL HISTORY Special education need .60(.49) .56(.50) .72(.45) No special education need 60.1 56.1 72.0

220

Learning disability 20.1 22.3 13.7 Behavioral problem 20.7 23.0 13.7 Mental retardation 0.6 0.7 0.5 ADHD 15.3 17.6 8.5 History of expulsions/suspensions since the first grade 1.54(.96) 1.61(.89) 1.33(1.11) None 11.2 9.2 17.0 One 12.6 11.5 16.1 Two or more 76.2 79.3 66.9 Age at first expulsion/suspension 1.49(.96) 1.56(.89) 1.28(1.10) None 11.2 9.2 17.0 14 to 18 18.0 16.8 21.5 13 or under 70.9 74.0 61.5 Enrolled in a community school during the last 6mos -1.51(1.31) -1.52(1.30) -1.47(1.35) Graduated/enrolled 87.7 88.0 86.9 Not enrolled 12.3 12.0 13.1 CURRENT SCHOOL STATUS Current school enrollment status -1.18(1.69) -1.15(1.73) -1.27(1.59) Full-time/graduated 72.6 72.2 73.6 Part-time 13.8 13.5 14.7 Drop-out, expelled, or suspended 13.6 14.2 11.7 Believes there is value in getting an education .43(1.11) .46(1.10) .32(1.12) Believes 35.3 33.8 39.9 Somewhat believes 51.2 52.1 48.3 Does not believe 13.5 14.1 11.9 Believes school provides an encouraging environment .85(1.07) .86(1.06) .83(1.08) Believes 21.9 21.6 23.0 Somewhat believes 49.2 49.4 48.5 Does not believe 28.9 29.0 28.5 School staff youth like/feels comfortable talking with -.59(.75) -.59(.75) -.60(.75) Two or more 16.1 16.2 15.9 One 27.2 26.8 28.5 None 56.6 57.0 55.6 Involvement in school activities in most recent term 1.17(1.14) 1.15(1.16) 1.23(1.11) Involved in two or more 4.0 4.1 3.4 Involved in one 11.5 11.9 10.6 Interested but not involved 32.4 32.7 31.5 Not interested 52.1 51.3 54.5 Conduct in most recent term 1.04(1.55) 1.09(1.55) .89(1.56) Good behavior 1.6 1.5 1.9 No problems 30.4 29.4 33.7 Problems reported by teachers 16.4 16.3 16.6 Problem calls to parents 33.6 33.6 33.3 Calls to police 18.1 19.2 14.6 Number of expulsions/suspensions in most recent term .24(1.34) .30(1.35) .06(1.29) None 50.2 48.0 56.9 One 30.6 31.5 27.8 Two or three 13.8 14.6 11.3 More than three 5.4 5.9 4.0 Attendance in most recent term 1.09(1.79) 1.01(1.80) 1.31(1.77) Good attendance 14.5 15.1 12.7 No unexcused absences 14.2 15.0 11.9 Some partial-day absences 18.2 18.8 16.1 Some full-day absences 25.7 25.8 25.5 Truant 27.4 25.3 33.8 Academic performance in most recent term .71(1.32) .74(1.30) .61(1.38) Mostly As 0.6 0.5 0.8 Mostly As and Bs 4.8 4.4 6.1 Mostly Bs and Cs 23.9 23.2 26.1 Mostly Cs and Ds 35.5 6.5 32.3 Some Ds and mostly Fs 35.2 35.4 34.7 Likelihood youth will stay/graduate from high school .80(1.01) .81(1.01) .75(1.02) Very likely 21.2 20.8 22.5 Uncertain 56.9 56.6 57.7 Not likely 21.9 22.7 19.8

221

HISTORIC USE OF FREE TIME Pro-social structured activities within past 5yrs -.93(.77) -.94(.77) -.89(.77) Involved in two or more 26.5 27.0 24.9 Involved in one 39.8 40.1 38.9 Never involved 33.7 32.9 36.1 Unstructured pro-social activities within past 5yrs -1.08(.74) -1.11(.74) -.99(.75) Involved in two or more 31.6 33.0 27.2 Involved in one 44.5 44.6 44.1 Never involved 23.9 22.3 28.7 CURRENT USE OF FREE TIME Interest/involvement pro-social structured activities -.80(.87) -.82(.88) -.75(.85) Involved in two or more 4.4 4.7 3.5 Involved in one 16.8 17.1 16.0 Interested but not involved 33.1 33.3 32.7 Not interested 45.6 44.9 47.8 Structured activities in which youth participates .21(.41) .21(.41) .19(.39) None 79.1 78.5 80.8 One or more 20.9 21.5 19.2 Interest/involvement pro-social unstructured activities -1.17(1.48) -1.24(1.47) -.95(1.50) Involved in two or more 19.5 20.7 15.8 Involved in one 33.0 34.3 29.1 Interested but not involved 19.9 19.1 22.2 Not interested 27.7 25.9 33.0 EMPLOYMENT HISTORY History of employment -.22(.42) -.23(.42) -.19(.40) Has been employed 22.2 23.1 19.4 Too young or never employed 77.8 76.9 80.6 History of successful employment -.79(.40) -.80(.40) -.77(.42) Yes 79.5 80.1 77.4 No 20.5 19.9 22.6 History of problems while employed .42(.66) .39(.65) .51(.71) Never fired/quit 68.2 69.9 61.9 Fired/quit: Poor performance 22.1 21.1 25.5 Fired/quit: Didn’t get along 9.8 9.0 12.6 History of positive personal relationship(s) with past -.89(.74) -.90(.74) -.88(.75) employer(s) or adult coworker(s) Two or more 22.5 22.5 22.8 One 44.2 44.7 42.5 None 33.2 32.8 34.7 CURRENT EMPLOYMENT Understanding of what is required to maintain a job -.64(.65) -.65(.65) -.62(.64) Demonstrated ability 9.5 9.8 8.8 Has knowledge 44.8 45.0 44.3 Lacks knowledge 45.6 45.2 46.9 Current interest in employment -.96(.93) -.98(.93) -.90(.92) Employed 5.7 5.9 5.1 Not employed, high interest 23.7 24.1 22.5 Not employed, some interest 31.4 31.8 30.1 Not interested or too young 39.2 38.2 42.3 Current employment status -.15(.51) -.09(.30) -.08(.29) Employment going well 9.2 9.4 8.5 Not employed 90.4 90.2 91.0 Problems with current employment 0.5 0.5 0.5 Current positive personal relationship(s) with -.15(.35) -.15(.35) -.14(.35) employer(s) or adult coworker(s) One or more positive relationships 14.5 14.6 14.4 Not employed, employed/no positive relationships 85.5 85.4 85.6 HISTORY OF RELATIONSHIPS History of positive adult non-family relationships not -.81(.92) -.81(.92) -.81(.91) connected to school or employment Three or more 7.1 7.2 6.9 Two 13.1 13.0 13.4 One 33.4 33.1 34.1 None 46.5 46.7 45.7

222

History of anti-social friends/companions 1.35(1.02) 1.38(1.05) 1.32(.94) Never had consistent friends 6.3 6.7 5.2 Only pro-social friends 74.8 74.5 75.7 Pro-social and anti-social friends 67.6 66.9 69.5 Only anti-social friends 86.0 85.3 88.4 Gang member 19.4 20.8 15.1 CURRENT RELATIONSHIPS Positive adult non-family relationships not connected -.73(.88) -.72(.88) -.73(.88) to school or employment Three or more 5.9 6.0 5.9 Two 11.2 11.1 11.3 One 32.5 32.3 32.8 None 50.5 50.6 50.1 Pro-social community ties -.64(.56) -.65(.56) -.63(.56) Strong ties 4.3 4.4 3.8 Some ties 55.7 55.7 55.5 None 40.1 39.9 40.7 Friends/companions youth spends time with 1.42(1.07) 1.43(1.10) 1.38(.99) No consistent friends 8.7 9.0 7.7 Only pro-social friends 64.3 64.6 64.0 Pro-social and anti-social friends 56.0 55.7 56.8 Only anti-social friends 82.8 82.1 85.2 Gang member 20.6 22.1 15.9 In a romantic, intimate, or sexual relationship -.03(.56) -.10(.51) .20(.63) Involved with prosocial person 17.0 18.8 11.9 Not involved 68.7 72.7 56.5 Involved with antisocial person 14.3 8.5 31.6 Admires anti-social peers .77(1.08) .76(1.08) .78(1.07) No 24.1 24.4 23.2 Somewhat 50.7 50.3 52.0 Yes 25.2 25.3 24.8 Resistance to anti-social peer influence .09(1.23) .07(1.24) .12(1.20) No association 10.3 10.8 8.6 Usually resists 33.9 33.7 34.4 Rarely resists 48.8 48.1 50.7 Leads antisocial peers 7.1 7.3 6.3 FAMILY HISTORY History of court-ordered or DSHS voluntary out-of- -.37(1.21) -.44(1.16) -.18(1.34) home and shelter care placements exceeding 30 days None 76.7 79.0 70.0 One 13.1 12.1 16.1 Two 4.0 3.6 5.5 Three or more 6.1 5.3 8.5 History of running away or getting kicked out of home 1.02(1.97) .74(1.91) 1.83(1.93) No history 43.3 49.3 25.3 One instance 13.1 13.3 12.6 Two to three 17.5 16.4 20.7 Four to five 7.2 6.1 10.5 Over five 18.9 14.9 30.8 History of petitions filed -.57(.82) -.63(.78) -.41(.91) No history 78.7 81.4 70.5 History 21.3 18.6 29.5 History of jail/imprisonment of persons in the .28(.96) .24(.97) .37(.93) household for at least 3mos No family imprisonment 36.3 37.9 31.5 Mother/female caretaker 31.3 28.9 38.2 Father/male caretaker 43.4 42.8 45.3 Sibling 21.2 20.4 23.6 Other family member 7.5 6.9 9.5 Has been living under any adult supervision -.95(.30) -.96(.28) -.94(.34) Yes 97.7 98.0 96.9 No 2.3 2.0 3.1 CURRENT LIVING ARRANGEMENTS Living with -.83(.39) -.84(.38) -.80(.42)

223

Living alone 0.1 0.1 0.1 Transient living 0.7 0.6 0.9 Mother 73.0 73.9 70.5 Father 45.5 47.3 39.6 Sibling(s) 59.5 60.6 56.4 Grandparent(s) 11.4 11.2 12.1 Other relative(s) 17.1 16.4 18.9 Foster/group home 4.9 4.3 6.7 Friends 1.8 1.6 2.2 Annual combined income youth & family .87(1.20) .84(1.21) .95(1.15) $50,000 and over 7.3 7.7 6.0 $35,000 to $49,000 11.9 12.1 11.0 $15,000 to $34,999 48.7 48.9 48.2 Under $15,000 32.2 31.3 34.8 Jail/imprisonment history of persons in the household -.08(1.00) -.10(1.00) -.01(1.00) No jail 53.7 54.9 49.9 Mother 20.0 18.5 24.5 Father 20.9 21.1 20.3 Sibling 15.1 14.8 16.0 Other family member 4.8 4.4 6.0 Problem history of parents in the household .04(1.00) .00(1.00) .14(.99) No problem history 48.4 50.2 43.1 Alcohol history 25.6 24.5 28.9 Drug history 22.0 20.9 25.5 Physical health history 16.5 16.0 18.0 Mental health history 14.2 13.1 17.6 Employment history 23.8 23.1 26.0 Problem history of sibling in the household -.25(.76) -.27(.76) -.18(.78) No siblings in the house 34.6 34.3 35.6 No problem history of siblings 44.0 45.5 39.7 Alcohol history 9.9 9.4 11.5 Drug history 13.0 12.5 14.6 Physical health history 1.7 1.6 2.1 Mental health history 5.4 5.0 6.8 Employment history 3.1 3.0 3.5 Support network for family -.97(.58) -.98(.58) -.93(.57) Strong 15.3 16.1 12.9 Some 66.2 65.8 67.3 None 18.5 18.1 19.8 Family willingness to help support youth .00(1.19) -.05(1.17) .16(1.23) Consistent willingness 56.0 58.1 49.6 Inconsistent support 35.6 34.1 40.2 Not willing 5.2 5.0 5.8 Hostile, berating, belittling 3.3 2.9 4.4 Family provides opportunities for youth to participate .78(.93) .76(.94) .85(.91) in family activities and decisions affecting youth Yes 19.0 19.8 16.6 Some 65.0 65.1 64.8 No 16.0 15.1 18.6 Has run away or been kicked out -.15(1.03) -.26(1.00) .15(1.05) No 58.9 63.8 44.3 Yes 38.6 34.2 51.8 Current runaway 2.5 2.0 3.9 Family member(s) youth feels close to or has good -.59(.81) -.62(.79) -.51(.86) relationship with (overall) Not close to family 20.1 18.8 23.8 Close to mother 49.9 51.9 43.8 Close to father 21.3 23.6 14.3 Close to male sibling 17.8 19.5 12.7 Close to female sibling 15.6 14.7 18.4 Close to extended family 19.2 19.1 19.7 Level of conflict in household .84(1.41) .74(1.40) 1.14(1.39) Some, well-managed 30.4 33.2 22.1 Verbal intimidation 42.9 42.5 43.9

224

Threats of physical abuse 8.9 8.6 9.7 Domestic violence 17.8 15.7 24.3 Parental supervision .49(1.22) .47(1.21) .56(1.22) Consistent 37.4 38.1 35.4 Sporadic 38.6 38.7 38.3 Inadequate 24.0 23.2 26.3 Parental authority and control .90(1.02) .86(1.03) 1.04(.97) Usually obeys 19.0 20.3 15.1 Sometimes obeys 52.9 53.5 51.0 Disobeys 28.2 26.3 33.9 Consistent appropriate consequences for bad behavior .30(1.45) .25(1.44) .44(1.45) Consistent: appropriate 54.6 56.3 49.6 Consistent: severe or insufficient 6.1 5.7 7.2 Inconsistent 39.3 38.0 43.2 Consistent appropriate reward for good behavior .45(1.27) .42(1.27) .55(1.26) Consistent: appropriate 41.2 42.4 37.5 Consistent: insufficient or indulgent 31.6 31.3 32.5 Inconsistent 27.2 26.3 30.0 Parent characterization of youth’s anti-social behavior -.50(.92) -.50(.92) -.50(.93) Disapproves 76.4 76.3 76.7 Minimizes 20.8 21.1 20.0 Accepts 2.6 2.5 3.1 Proud of 0.2 0.2 0.3 ALCOHOL & DRUG HISTORY History of alcohol use (overall) .17(1.39) .11(1.41) .34(1.33) No past alcohol use 22.7 24.5 17.5 Past alcohol use 77.3 75.6 82.4 Disrupted education 21.9 20.7 25.6 Caused family conflict 27.3 25.6 32.3 Interfered with pro-social friends 24.0 22.6 28.4 Caused poor health 4.4 3.8 6.2 Contributed to criminal behavior 22.3 21.7 24.1 Alcohol tolerance &/or withdrawal 3.6 .04(.19) 3.2 .03(.18) 4.8 .05(.21) History of drug use (overall) 1.51(2.07) 1.49(2.10) 1.57(1.98) No past drug use 19.4 20.2 17.0 Past drug use 80.7 79.9 83.0 Disrupted education 36.3 36.0 37.3 Caused family conflict 37.2 36.2 40.2 Interfered with pro-social friends 34.1 33.1 37.0 Caused health problems 5.5 4.7 8.0 Contributed to criminal behavior 28.6 29.0 27.5 Drug tolerance &/or withdrawal 7.7 .08(.27) 7.2 .07(.26) 9.1 .09(.29) History of referrals for drug/alcohol assessment .98(1.24) .96(1.23) 1.03(1.26) No problem or never referred 57.2 57.7 55.7 Referred but not assessed 8.3 8.3 8.3 Diagnosed as abuse 13.8 13.9 13.5 Diagnosed as dependent 20.7 20.1 22.5 History of attending alcohol/drug education classes -.45(.79) -.45(.79) -.46(.80) Voluntarily attended 2.9 2.8 3.0 Attended at request 10.6 10.6 10.6 Attended at court direction 15.3 15.2 15.7 Never attended 71.2 71.4 70.7 History of participating in alcohol/drug treatment -.26(.44) -.26(.44) -.27(.45) Participated 26.3 25.9 27.3 Has not participated 73.7 74.1 72.7 Youth using alcohol/drugs -.11(1.79) -.11(1.79) -.09(1.78) No 27.7 27.8 27.3 Yes 72.3 72.2 72.7 CURRENT ALCOHOL & DRUGS Alcohol use (overall) 1.21(1.19) 1.19(1.20) 1.28(1.17) No current alcohol use 29.7 30.5 27.2 Not disrupting functioning 43.8 42.8 46.8 Disrupts education 14.3 13.7 16.0 Causes family conflict 19.1 18.2 21.8

225

Interferes with prosocial friends 16.9 16.2 19.2 Causes health problems 3.2 2.9 4.3 Contributes to criminal behavior 16.7 16.7 16.7 Alcohol tolerance &/or withdrawal 2.5 .03(.16) 2.3 .02(.15) 3.3 .03(.18) Drug use (overall) 2.29(1.39) 2.32(1.39) 2.19(1.40) No current drug use 14.8 14.3 16.5 Not disrupting functioning 59.2 59.6 58.1 Disrupts education 28.0 28.2 27.6 Drug use causes family conflict 30.3 30.2 30.9 Interferes with prosocial friends 27.6 27.2 28.6 Causes health problems 4.8 4.1 6.7 Contributes to criminal behavior 23.5 24.2 21.7 Drug tolerance &/or withdrawal 6.8 .07(.25) 6.4 .06(.25) 7.8 .08(.27) Type of drugs currently used Marijuana 57.6 .58(.49) 58.4 .58(.49) 55.4 .55(.50) Amphetamines 9.5 .09(.29) 7.6 .08(.27) 14.9 .15(.36) Cocaine 5.1 .05(.22) 4.6 .05(.21) 6.7 .07(.25) Heroin 1.8 .02(.13) 1.4 .01(.12) 3.1 .03(.17) Other drug 9.0 .09(.29) 8.5 .08(.28) 10.7 .11(.31) Alcohol/drug treatment participation .34(.84) .35(.83) .31(.84) Successfully completed 2.2 2.1 2.4 Currently attending 17.1 16.9 17.9 Treatment not warranted 25.4 25.1 26.5 Needs treatment, not attending 55.3 55.9 53.3 MENTAL HEALTH HISTORY History of suicidal ideation No thoughts of suicide 73.6 .74(.44) 78.6 .79(.41) 58.9 .59(.49) Serious thoughts of suicide 17.4 .17(.38) 14.6 .15(.35) 25.8 .26(.44) Has made a plan 3.0 .03(.17) 2.3 .02(.15) 4.8 .05(.21) Has attempted 6.9 .07(.25) 4.7 .05(.21) 13.4 .13(.34) Hopeless 3.8 .04(.19) 3.1 .03(.17) 6.0 .06(.24) Self-mutilating 3.8 .04(.19) 2.2 .02(.15) 8.7 .09(.28) History of physical abuse (overall) -.36(.93) -.43(.91) -.17(.98) Not a victim of physical abuse 68.7 71.8 59.4 Physical abuse: family member 22.2 20.4 27.8 Physical abuse: in the home 9.0 8.0 11.9 Physical abuse: someone outside the family 8.3 6.7 13.2 Physical abuse: foster home 0.5 0.5 0.7 Physical abuse: with a weapon 1.0 1.1 1.0 Has not witnessed violence 10.5 .10(.31) 11.0 .11(.31) 9.0 .09(.29) Witnessed violence in the home 18.8 .19(.39) 17.3 .17(.38) 23.4 .23(.42) Witnessed violence in foster home 1.1 .01(.10) 0.9 .01(.10) 1.5 .02(.12) Witnessed violence in the community 19.8 .20(.40) 19.2 .19(.39) 21.5 .22(.41) Witnessed murder 0.7 .01(.08) 0.7 .01(.08) 0.6 .01(.08) History of sexual abuse (overall) -.71(.71) -.84(.54) -.31(.95) Not a victim of sexual abuse 85.6 92.1 66.1 Sexual abuse: family member 6.6 4.1 13.9 Sexual abuse: someone outside the family 9.3 4.3 23.9 History of being a victim of neglect 25.9 -.48(.88) 23.8 -.52(.85) 32.2 -.36(.93) History of ADD/ADHD -.32(1.06) -.25(1.09) -.56(.92) No 69.9 66.4 80.4 Medication or treatment prescribed 22.7 25.4 14.4 Medication and treatment prescribed 7.5 8.2 5.2 History of mental health problems -.25(1.16) -.32(1.12) -.06(1.23) No 69.4 72.0 61.6 Medication or treatment prescribed 17.3 15.9 21.5 Medication and treatment prescribed 13.3 12.1 16.9 Health insurance -.88(.47) -.87(.49) -.90(.43) Yes 94.1 93.7 95.1 No 5.9 6.3 4.9 Current mental health problem status -.40(.92) -.45(.90) -.27(.96) No 70.1 72.3 63.5 Yes 29.9 27.7 36.5 Anger 1.49(.96) 1.45(.96) 1.60(.94)

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No history 12.8 14.0 9.5 Occasional feelings 45.6 46.4 43.4 Consistent feelings 21.2 20.0 24.6 Aggressive reactions 20.4 19.6 22.5 Depression 1.02(.83) .93(.81) 1.27(.84) No history 28.7 32.6 18.0 Occasional feelings 44.9 45.1 44.4 Consistent feelings 21.7 18.6 30.4 Impairment in daily tasks 4.6 3.7 7.2 Somatic complaints .32(.64) .28(.61) .43(.71) No history 75.4 78.4 66.9 One or two 19.4 17.0 26.2 Three or four 3.0 2.6 4.1 Five or more 2.2 2.0 2.8 Delusions/hallucinations .05(.21) .04(.20) .05(.22) No 95.5 95.7 95.0 Yes 4.5 4.3 5.0 Trauma .39(.49) .34(.48) .52(.50) No 60.9 65.6 48.0 Yes 39.1 34.4 52.0 CURRENT MENTAL HEALTH Suicide ideation No recent thoughts 22.8 .23(.42) 19.4 .19(.40) 30.9 .31(.46) Recent plan 0.8 .01(.09) 0.6 .01(.08) 1.4 .01(.12) Recent attempt 1.3 .01(.11) 0.9 .01(.09) 2.6 .03(.16) Hopeless 0.9 .01(.10) 0.7 .01(.09) 1.5 .02(.12) Self-mutilation 1.1 .01(.11) 0.6 .01(.08) 2.6 .03(.16) Diagnosed with ADD/ADHD -.09(.61) -.11(.66) -.04(.48) Compliant with medication 23.7 28.1 13.7 No problem or no medication 61.6 54.9 76.7 Non-compliant with medication 14.8 17.0 9.6 Treatment prescribed, excluding ADD/ADHD -.21(.75) -.22(.73) -.20(.80) treatment Attending treatment 41.4 40.1 44.1 No treatment need 38.6 41.5 32.0 Non-compliant with treatment 20.0 18.4 23.8 Mental health medication prescribed, excluding -.21(.67) -.23(.66) -.18(.70) ADD/ADHD medication Compliant with medication 35.4 35.6 35.0 No medication need 50.1 51.2 47.6 Non-compliant with medication 14.4 13.1 17.4 Mental health problems interfere with working with .32(.47) .33(.47) .31(.46) the youth No problem or mental health does not interfere 67.9 67.2 69.5 Yes 32.1 32.8 30.5 ATTITUDES/BEHAVIORS Primary emotion when committing last crime(s) .64(.77) .65(.76) .61(.79) within last 6mos Nervous, afraid, worried, uncertain 18.0 17.6 19.3 Hyper, excited, stimulated, confident, unconcerned 82.0 82.4 80.7 Primary purpose for committing crime(s) in last 6mos Anger/revenge 15.8 .16(.44) 23.4 .23(.42) 33.1 .33(.47) Power 1.1 .01(.10) 1.0 .00(.06) 1.3 .01(.11) Impulse 19.0 .19(.39) 19.2 .19(.39) 18.4 .18(.39) Sexual desire 3.0 .03(.17) 3.9 .04(.19) 0.4 .00(.06) Money, material gain, or drugs 18.8 .19(.39) 19.3 .19(.39) 17.4 .17(.38) Excitement, amusement 15.3 16.0 13.3 Status, acceptance, attention 15.8 16.2 14.7 Optimism -.30(1.06) -.28(1.06) -.34(1.07) High aspirations 5.7 5.1 7.4 Normal aspirations 57.1 57.2 56.8 Low aspirations 35.7 36.2 34.4 Believes nothing matters 1.5 1.5 1.4 Impulsive; acts before thinking .29(1.27) .30(1.27) .28(1.27)

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Uses self-control 4.8 4.6 5.3 Some self-control 37.4 37.4 37.2 Impulsive 39.5 39.5 39.6 Highly impulsive 18.4 18.5 17.9 Belief in control over anti-social behavior .18(1.44) .18(1.44) .19(1.45) Believes 29.7 29.7 29.8 Somewhat believes 62.6 62.8 62.2 Does not believe 7.6 7.5 8.0 Empathy, remorse, sympathy, or feelings for victim(s) -.15(1.59) -.14(1.59) -.16(1.59) Empathy 17.5 17.3 18.0 Some empathy 48.2 48.3 47.9 No empathy 34.3 34.3 34.1 Respect for property of others .80(1.77) .85(1.76) .65(1.80) Respects 25.9 24.8 29.1 Respects personal, not public 30.1 30.0 30.4 Conditional respect for personal 30.4 30.9 28.9 No respect 13.6 14.3 11.6 Respect for authority figures .01(1.81) .02(1.82) -.02(1.80) Respects 42.7 42.6 43.1 Does not respect 35.3 35.1 36.1 Resents 15.1 15.4 14.3 Defies or is hostile 6.8 6.9 6.5 Attitude toward pro-social rules/conventions in society .73(1.41) .74(1.41) .69(1.41) Abides 18.4 18.3 18.9 Believes rules sometimes apply 59.5 59.2 60.5 Does no believe rules apply 16.1 16.5 15.0 Resents rules 5.9 6.0 5.6 Accepts responsibility for anti-social behavior .34(1.60) .33(1.61) .36(1.60) Accepts responsibility 29.9 30.1 29.4 Minimizes antisocial behavior 50.3 50.3 50.5 Accepts antisocial behavior 15.8 15.6 16.3 Proud of antisocial behavior 4.0 4.0 3.9 Belief in meeting conditions of court supervision .06(1.07) .06(1.07) .04(1.06) Believes 49.5 49.3 49.9 Unsure 46.0 46.0 45.8 Does not believe 4.6 4.7 4.3 AGGRESSION Tolerance for frustration .78(1.32) .71(1.35) .98(1.21) Rarely upset 16.8 18.4 12.3 Sometimes upset 54.7 55.3 52.9 Often upset 28.5 26.4 34.9 Hostile interpretation of actions & intentions of others -.19(1.59) -.22(1.59) -.12(1.58) Positive view 42.9 43.6 40.6 Negative view 47.8 47.3 49.5 Hostile view 9.3 9.1 10.0 Belief in verbal aggression to resolve a conflict .68(1.36) .62(1.39) .87(1.28) Rarely appropriate 19.0 20.4 14.8 Sometimes appropriate 55.9 56.5 54.0 Often appropriate 25.1 23.1 31.1 Belief in physical aggression to resolve a conflict .67(1.81) .66(1.81) .69(1.82) Never appropriate 14.3 14.2 14.5 Rarely appropriate 29.4 29.7 28.7 Sometimes appropriate 44.4 44.4 44.3 Often appropriate 11.9 11.7 12.5 Evidence of violence not in criminal history No reports 45.3 .45(.50) 46.2 .46(.50) 42.9 .43(.49) Violent destruction of property 15.3 .15(.36) 15.4 .15(.36) 14.9 .15(.36) Violent outbursts, displays of temper, uncontrolled 50.4 .50(.50) 49.1 .49(.50) 54.3 .54(.50) anger indicating potential for harm Deliberately inflicted physical pain 16.5 .17(.37) 16.0 .16(.37) 18.1 .18(.39) Used/threatened with a weapon 7.5 .08(.26) 8.2 .08(.27) 5.4 .05(.23) Fire starting reports 4.1 .04(.20) 4.7 .05(.21) 2.2 .02(.15) Animal cruelty reports 1.4 .01(.12) 1.6 .02(.12) 0.9 .01(.10) Evidence of sexual aggression not in criminal history

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No reports of outside of criminal history 96.7 .97(.18) 96.1 .96(.19) 98.4 .98(.13) Reports of aggressive sex 1.0 .01(.10) 1.1 .01(.11) 0.5 .01(.07) Reports of sex for power 0.4 .00(.06) 0.4 .00(.06) 0.4 .00(.06) Reports of young sex partners 1.0 .01(.10) 1.3 .01(.11) 0.3 .00(.05) Reports of child sex 1.0 .01(.10) 1.2 .01(.11) 0.2 .00(.05) Reports of voyeurism 0.3 .00(.06) 0.4 .00(.06) 0.1 .00(.04) Reports of exposure 0.9 .01(.09) 0.9 .01(.10) 0.6 .01(.08) SKILLS Consequential thinking -.99(.89) -.98(.89) -1.03(.89) Acts to obtain desired consequences 3.3 3.2 3.8 Identifies consequences of actions 17.0 16.7 18.1 Understands there are consequences to actions 67.5 67.8 66.6 Does not understand 12.1 12.3 11.5 Goal-setting .09(1.44) .13(1.45) -.01(1.43) Realistic goals 8.5 8.0 10.1 Somewhat realistic goals 46.6 46.2 47.8 Unrealistic goals 17.0 16.9 17.2 No goals 28.0 29.0 24.8 Problem-solving -.60(1.13) -.58(1.13) -.64(1.13) Applies appropriate solutions 2.5 2.4 2.8 Thinks of solutions 14.5 14.0 15.8 Identifies problem behaviors 53.1 53.2 52.8 Cannot identify 29.9 30.4 28.5 Situational perception -.59(1.22) -.57(1.22) -.65(1.22) Selects best time and place 4.2 4.0 4.6 Chooses skill but not time/place 16.7 16.3 18.0 Analyzes but unable to choose skill 46.2 46.1 46.5 Cannot analyze 32.9 33.6 31.0 Dealing with others -.79(1.08) -.77(1.08) -.86(1.08) Often uses advanced skills 2.6 2.4 3.2 Sometimes uses advanced skills 19.2 18.5 2.4 Has basic skills, not advanced 55.5 55.9 54.5 Lacks basic skills 22.6 23.2 20.9 Dealing with difficult situations .65(1.32) .66(1.31) .62(1.33) Often uses skills 2.3 2.3 2.5 Sometimes uses skills 32.1 31.7 33.3 Rarely uses skills 28.9 29.3 27.7 Lacks skills 36.6 36.7 36.4 Dealing with feelings/emotions .73(1.29) .74(1.29) .68(1.31) Often uses skills 2.1 2.1 2.4 Sometimes uses skills 29.5 29.0 31.0 Rarely uses skills 30.5 30.9 29.2 Lacks skills 37.9 38.1 37.4 Monitoring of internal triggers .64(1.53) .65(1.53) .60(1.54) Actively monitors 3.1 3.0 3.4 Identifies 41.3 40.9 42.3 Cannot identify 55.6 56.1 54.4 Monitoring of external triggers .14(1.51) .15(1.51) .11(1.51) Actively monitors 4.1 4.0 4.3 Identifies 56.6 56.5 57.2 Cannot identify 39.3 39.5 38.5 Control of impulsive behaviors .78(1.66) .78(1.66) .76(1.66) No problem 4.7 4.7 4.5 Uses techniques 3.0 3.0 3.2 Knows techniques 29.0 28.9 29.5 Lacks techniques 63.3 63.4 62.9 Control of aggression .05(1.78) .03(1.79) .11(1.78) No problem 10.5 10.7 9.8 Often uses alternatives 9.5 9.7 9.0 Sometimes uses alternatives 30.0 30.1 29.8 Rarely uses alternatives 14.4 14.5 14.3 Lacks alternatives 35.6 35.1 37.2

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APPENDIX C

WASHINGTON STATE PACT EBP ELIGIBILITY

Coordination of Services: All low-risk youth as indicated by the pre-screen or full risk assessment

Education and Employment Training (EET): 1. Age 15 to 18 and any one of the following: • Domain 3A (School History) static risk score is 4 or 5 • Domain 3B (Current School Status) dynamic risk score is between 7 and 22 • Domain 5A (Employment History) static protective factor is 0 or 1 • Domain 5B (Current Employment) dynamic protective factor is 0 to 2 2. Even though the above is the currently eligibility for EET in PACT, the CJAA committee has decided that only moderate and high risk will be eligible for EET.

Family Functional Therapy (FFT) 1. Risk level moderate or high and 2. Domain 7B (Currently Living Arrangements) items 7-16: dynamic risk score of at least 6

Family Integrated Transitions (FIT) 1. Risk level is moderate or high and 2. Domain 7B (Current Living Arrangements): dynamic risk score is equal or greater than 8 and 3. Domain 9A (Mental Health History) agreement to one or more: a. History of Suicidal Ideation (item 1) b. History of Mental Health Problems (item 7) c. Current Mental health problem status (item 14) or Domain 9B (Current Mental Health) agreement to one or more: d. Current Suicidal Ideation (item 1) e. Mental Health treatment current prescribed (item 3) f. Mental Health medication currently prescribed (item 4) g. Mental Health problems currently interfere with the youth (item 5) 4. And any one of the following: a. Domain 8A, item 1, “History of alcohol use” any response except “No past alcohol use” b. Domain 8A, item 2, “History of drug use” any response except “No past drug use” c. Domain 8A, item 6, “Minor is currently using alcohol and/or drugs” response = yes Multisystemic Therapy (MST) 1. Risk level is high and 2. Domain 7B (Current Living Arrangements): dynamic risk score is equal or greater than 8

Washington State Aggression Replacement Training (WSART) 1. Risk level is moderate or high and any one of the following: • Domain 1 (Criminal History) static risk factor score of at least 1 for o Weapon (item 4) or o Violent misdemeanor (item 5) or o Violent felony (item 6) • Domain 11 (Aggression) items 2, 3 and 4: dynamic risk factor of at least 2 • Domain 10 (Attitudes/Behaviors) items 6-10: dynamic risk score of at least 5 • Domain 12 (Skills) all items except 2: dynamic risk score of at least 4

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APPENDIX D

PACT ITEMS USED TO CREATE ACES MEASURES10

ACE Study Measures PACT ACE Measures Measure 1: Emotional Abuse 1. How often did a parent, stepparent, or 1. Family willingness to help support youth: adult living in your home swear at you, a. Consistently willing to support youth insult you, or put you down? b. Inconsistently willing to support youth 2. How often did a parent, stepparent, or c. Little or no willingness to support youth adult living in your home act in a way d. Hostile, berating, and/or belittling of youth that made you afraid that you might be physically hurt? 2. Level of conflict between parents, between youth and parents, among siblings: a. Some conflict that is well managed Identification: A respondent was defined as b. Verbal intimidation, yelling, heated arguments being emotionally abused during childhood c. Threats of physical abuse if the response was either often or very d. Domestic violence: physical/sexual abuse often to question 1 or sometimes, often or very often to question 2. Identification: A respondent would be defined as being emotionally abused during childhood if the response was either d on the first question or answers b or c on the second question. Measure 2: Physical Abuse 1. How often did a parent, stepparent, or 1. History of violence/physical abuse: (Includes suspected incidents of abuse, adult living in your home push, grab, whether or not substantiated but excludes reports proven to be false): slap or throw something at you? a. Not a victim of violence/physical abuse 2. How often did a parent, stepparent, or b. Victim of violence/physical abuse at home adult living in your home hit you so c. Victim of violence/physical abuse in a foster/group home hard that you had marks or were d. Victimized or physically abused by family member injured? e. Victimized or physically abused by someone outside the family f. Attacked with a weapon Identification: A respondent was defined as being physically abused during childhood if 2. Level of conflict between parents, between youth and parents, among siblings: the response was either sometimes, often or a. Some conflict that is well managed very often to question 1 or if there was any b. Verbal intimidation, yelling, heated arguments response other than never to question 2. c. Threats of physical abuse d. Domestic violence: physical/sexual abuse

Identification: A respondent would be defined as being physically abused during childhood if the response was any response other than a on question 1. A respondent would be defined as physically abused if question 2, response d was yes, but only when the same youth gave negative answers to a question of history of sexual abuse/rape. Measure 3: Sexual Abuse Each respondent was asked whether an 1. History of sexual abuse/rape: (Includes suspected incidents of abuse if adult, relative, family friend, or stranger disclosed by youth, whether or not reported or substantiated, but excludes who was at least 5 years older than the reports proven to be false): respondent had ever: a. Not a victim of sexual abuse/rape b. Sexually abused/raped by family member 1. Touched or fondled the respondent’s c. Sexually abused/raped by someone outside the family body in a sexual way; 2. Had the respondent touch his or her 2. Level of conflict between parents, between youth and parents, among siblings: body in a sexual way; a. Some conflict that is well managed 3. Attempted to have any type of sexual b. Verbal intimidation, yelling, heated arguments intercourse (oral, anal, or vaginal) with c. Threats of physical abuse the respondent; or d. Domestic violence: physical/sexual abuse

10 Adapted from Baglivio et al. (2014).

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4. Actually had any type of sexual Identification: A respondent would be defined as being sexually abused during intercourse (oral, anal, or vaginal) with childhood if the response was any response other than a on question 1. the respondent. Additionally, a respondent would be defined as sexually abused if question 2 was answered with a yes to d, but only when the same juvenile gave negative answers Identification: A respondent would be to a question of history of physical abuse. defined as being physically abused during childhood if the response was any response other than a on question 1. A respondent would be defined as physically abused if question 2, response d was yes, but only when the same youth gave negative answers to a question of history of sexual abuse/rape. Measure 4: Emotional Neglect Questions used to define emotional neglect 1. Support network for family: Extended family and/or family friends who can were adapted from the Childhood Trauma provide additional support to the family: Questionnaire (CTQ). Five CTQ items were a. No support network used. Response categories were never true, b. Some support network rarely true, sometimes true, often true, and c. Strong support network very often true. These items were scored on a Likert scale ranging from 1 to 5, 2. Family willingness to help support youth: respectively. For emotional neglect, all a. Consistently willing to support youth items were reverse scored, then added. b. Inconsistently willing to support youth c. Little or no willingness to support youth *The neglect questions/scales were d. Hostile, berating, and/or belittling to youth developed for the Wave 2 survey, and some of the earlier studies do not use the neglect 3. Family members youth feels close to or has a good relationship with: measures. a. Does not feel close to any family member b. Feels close to mother/female caretaker 1. There was someone in my family who c. Feels close to father/male caretaker helped me feel important or special d. Feels close to male sibling 2. I felt loved e. Feels close to female sibling 3. People in my family looked out for f. Feels close to extended family each other 4. People in my family felt close to each Identification: A respondent would be defined as being emotionally neglected if other the response to question 1 was a or the response to question 2 was c or d, or the 5. My family was a source of strength response to question 3 was a. and support

Identification: Scores of 15 or higher (moderate to extreme on the CTQ clinical scale) defined the respondents as having experienced emotional neglect. Measure 5: Physical Neglect Questions used to define physical neglect 1. History of being a victim of neglect*: were adapted from the Childhood Trauma a. Not a victim of neglect Questionnaire (CTQ). Five CTQ items were b. Victim of neglect used. Response categories were never true, rarely true, sometimes true, often true, and Identification: A respondent would be defined as being physically neglected if the very often true. These items were scored on response to question 1 was b. a Likert scale ranging from 1 to 5, respectively. For physical neglect, items 2 *Neglect includes the negligent or dangerous act or omission that constitutes a and 5 were reverse-scored, and all five clear and present danger to the child’s health, welfare, or safety, such as: Failure scores were added. to provide adequate food, shelter, clothing, emotional nurturing, or health care.

1. I didn’t have enough to eat 2. I knew there was someone there to take care of me and protect me 3. My parents were too drunk or too high to take care of me

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4. I had to wear dirty clothes 5. There was someone to take me to the doctor if I needed it

Identification: Scores of 10 or higher (moderate to extreme on the CTQ clinical scale) defined the respondents as having experienced physical neglect. Measure 6: Family Violence Battered mother (Was your mother [or 1. Level of conflict between parents, between youth and parents, among stepmother]): siblings: a. Some conflict that is well managed 1. Sometimes, often, or very often pushed b. Verbal intimidation, yelling, heated arguments grabbed, slapped, or had something c. Threats of physical abuse thrown at her? d. Domestic violence: physical/sexual abuse 2. Sometimes, often, or very often kicked, bitten, hit with a fist, or hit 2. History of witnessing violence: with something hard? a. Has not witnessed violence 3. Ever repeatedly hit over at least a few b. Has witnessed violence at home minutes? c. Victim of violence/physical abuse in a foster/group home 4. Ever threatened with or hurt by a knife d. Has witnessed violence in a foster/group home or gun? e. Has witnessed violence in the community f. Family member killed as a result of violence Identification: A respondent would be identified as having a history of household Identification: A respondent would be defined as having a history of household dysfunction if any response to questions 1– dysfunction if the response to question 1 were b, c, or d, or if the response to 4 was affirmative. question 2 was positive for b, or d. Measure 7: Household Substance Abuse 1. As a child, did you ever: Live with 1. Problem history of parents who are currently involved with the household: anyone who was a problem drinker or a. No problem history of parents in household alcoholic? b. Parental alcohol problem history 2. As a child, did you ever: Live with c. Parental drug problem history anyone who used street drugs? d. Parental physical health problem history e. Parental mental health problem history Identification: A respondent would be f. Parental employment problem history defined as having a history of household substance abuse if a response to either 2. Problem history of siblings who are currently involved with the household: question was affirmative. a. No siblings currently in household b. No problem history of siblings in household c. Sibling alcohol problem history d. Sibling drug problem history e. Sibling physical health problem history f. Sibling mental health problem history g. Sibling employment problem history

Identification: A respondent would be defined as having a history of household substance abuse if responses b or c in question 1, or responses c or d in question 2 was identified. Measure 8: Household Mental Illness 1. Was a household member depressed or 1. Problem history of parents who are currently involved with the household: mentally ill? a. No problem history of parents in household 2. Did a household member attempt b. Parental alcohol problem history suicide? c. Parental drug problem history d. Parental mental health problem history Identification: A respondent would be e. Parental physical health problem history defined as having a history of household f. Parental employment problem history mental illness if a response to either question was affirmative. 2. Problem history of siblings who are currently involved with the household: a. No siblings currently in household b. No problem history of siblings in household

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c. Sibling alcohol problem history d. Sibling drug problem history e. Sibling mental health problem history f. Sibling physical health problem history g. Sibling employment problem history

Identification: A respondent would be defined as having a history of household mental illness if response d in question 1 or response e in question 2 was identified. Measure 9: Parental Separation/Divorce 1. Were your parents ever separated or 1. All persons with whom the youth is currently living: divorced? a. Living alone b. Transient (street) Identification: A respondent would be c. Biological mother identified as having a history of parental d. Biological father separation/divorce if the question was e. Nonbiological mother answered affirmatively. f. Nonbiological father g. Older sibling(s) h. Younger sibling(s) i. Grandparent(s) j. Other relative(s) k. Long-term parental partner(s) l. Short-term parental partner(s) m. Youth’s romantic partner n. Youth’s child o. Foster/group home p. Youth’s friends

Identification: A respondent would be defined as having a history of parental separation/divorce if responses c and d are not both selected. Measure 10: Incarcerated Household Member 1. Did a household member go to prison? 1. History of jail/imprisonment of persons who were ever involved in the household for at least 3 months: Identification: A respondent would be a. No jail/imprisonment history in family defined as having a history of an b. Mother/female caretaker incarcerated household member if the c. Father/male caretaker question was answered affirmatively. d. Sibling drug problem history e. Older sibling f. Younger sibling g. Other member

2. Jail or prison history of persons who are currently involved in the household: a. No jail/imprisonment history in family b. Mother/female caretaker c. Father/male caretaker d. Sibling drug problem history e. Older sibling f. Younger sibling g. Other member

Identification: A respondent would be defined as having a history of an incarcerated household member if any response other than a for question 1 or question 2 was identified.

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