Examining the Association Between Attachment and Sexual HIV-Risk Behaviors

Examining the Association between Attachment and Sexual HIV-Risk Behaviors

among African American Young Women

A thesis submitted to the

Graduate School

of the University of Cincinnati

in partial fulfillment for the degree of

Master of Arts

in the Department of Psychology

of the College of Arts and Sciences

Nicole Gause

M.A. Columbia University

June 2014

B.A. Boston University

June 2011

Committee: Jennifer L. Brown, Ph.D. (Chair)

Sarah Whitton, Ph.D.

Kristen Jastrowski Mano, Ph.D.

Abstract

African American women are disproportionately affected by HIV/STI. Extant literature suggests that attachment style may be associated with risky sexual behavior. Working model-self

(WMS) and working model-other (WMO) are attachment-related constructs that reflect beliefs about oneself in relationships, and beliefs about others with whom one relates to or interacts with. The present study examined the associations between WMS, WMO and sexual risk behaviors: (a) proportion of condom use for vaginal and anal sex during past 3 months, (b) number of sexual partners in lifetime and during the past 3 months, (c) having a casual sexual partner during the past 3 months, and (d) using alcohol before having sex during the past 3 months. Potential partial mediators of the relationship between WMS, WMO and sexual risk behaviors were also assessed, including: (a) partner communication self-efficacy, (b) fear of condom negotiation, (c) peer norms for risky sexual behavior, (d) partner trust, and (e) sex- related alcohol expectancies. Structural equation modeling analyses assessed a model of the

Theory of Gender and Power (TGP) with the added attachment construct to explain risky sexual behaviors among African American women. A total of 560 participants completed the baseline assessment for an HIV prevention intervention trial and were randomized to study conditions.

The current study includes only the baseline data obtain from these 560 participants (M age=

20.58, SD = 1.89). Participants self-reported sociodemographics, sexual history, alcohol use, communication skills, and psychosocial constructs associated with STI/HIV-preventive behaviors via an audio computer-assisted self-interview (ACASI) survey. Results indicated that

WMS (but not WMO) was associated with number of lifetime and recent (during the past 3 months) sexual partners; however, WMS was not associated with having a recent causal sexual partner, proportion condom use, or consuming alcohol prior to sexual encounters. Furthermore,

WMS (but not WMO) was associated with psychosocial factors associated with engagement in risky sexual behaviors; specifically, partner communication self-efficacy, fear of condom negotiation, peer norms for risky sexual behavior, partner trust, and sex-related alcohol expectancies. These psychosocial factors are often the targets of HIV prevention interventions for African American women. Structural equation modeling revealed that the Attachment construct (indicated by WMS and WMO) was associated with both social and physical exposures that increase probability of contracting HIV/STI according to the Theory of Gender and Power.

These exposures are associated with engagement in behaviors that increase HIV/STI-risk.

Results indicate that women may benefit from interventions that targets these core schemas-like beliefs (WMS) prior to addressing the psychosocial factors that are associated with WMS and risky sexual behaviors. Furthermore, findings may inform HIV prevention efforts in two ways:

(1) identifying women who may be exposed to the TGP social and physical factors that increase probability of contracting HIV/STI, and (2) understanding why some women are resistant to HIV prevention intervention that address Risk Factors (i.e., behaviors), but not the social or physical exposures that may precede or maintain with these behaviors. Future research should seek to clarify the manner in which WMS may affect HIV prevention intervention efforts.

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Table of Contents

Page Introduction………………………………………………………………………………………1 Attachment Theory: Definition and Development of Attachment…………………..……4 Adult Attachment…………………………………………………………………….……7 Associations between Attachment Style & Risky Sexual Behavior……………..………24 Potential Mediators of the Relationship Between Attachment and Risky Sexual Behavior……………………………………………………………………….…………34 The Theory of Gender and Power as Applied to HIV-risk Disparities between Males and Females………………………………………………………………………………...... 41 Emerging Research Priorities……………………………………………………………52

Aims & Hypotheses……………………….…………………………………………………….56 Aim 1a……………………………………………………………………………………56 Aim 1b…………………………………………………………………………………...57 Aim 1c……………………………………………………………………………………57 Aim 2………………………………….…………………………………………………59 Aim 3………………………………….…………………………………………………60

Method…………………………………………………………………………………………..62 Participants…………………………..………………….………………………………..62 Procedures………………………………………………………….…………………….62 Measures…………………………………………………………………………………63

Data Analytic Approach……………………………………………………………………….71 Preliminary Data Examination……………………………………………………..…….71 Descriptive Statistics……………………………………………………………………..72 Aim 1a……………………………………………………………………………………73 Aim 1b………………………………………………………………………………...…75 Aim 1c………………………………………………………………………………....…79 Aim 2……………………………………………………………………………….....…81 Aim 3……………………………………………………………………………….....…88

Results……………………………………………………………………………………….…..92 Descriptive Univariate Statistics…………………………………………………………92 Aim 1a Multiple Regression Analyses…………………………………………………..93 Aim 1b Simple Regression Analyses…………………………………………………….95 Aim 1c Moderation Analyses………………………………………………………...….97 Aim 2 Mediation Analyses………………………………………………………...…….98 Aim 3 Confirmatory Factor Analysis and Structural Equation Modeling Analyses…...105

Discussion……………………………………………………………………………………...111 Limitations……………………………………………………………………………...117 Future Directions…………………………………………………………………….…119 Conclusions……………………………………………………………………………..122

List of Tables and Figures

Table Page Table 1……………………………………………………………………………………….…149 Demographic Characteristics of Participants (N =560)

Table 2……………………………………………………………………………………….…150 Working Model-Self (WMS) and Working Model-Other (WMO) Scores by Dominant Attachment

Table 3……………………………………………………………………………………….…151 Sexual Risk Behaviors Among Participants (N=560)

Table 4……………………………………………………………………………………….…152 Theory of Gender and Power (TGP) Construct Characteristics Among Participants (N=560)

Table 5……………………………………………………………………………………….…153 Relationship Between WMS and WMO and Sexual Risk Behaviors: Results from Multiple Regression Models

Table 6……………………………………………………………………………………….…155 Relationship Between WMS and Sexual Risk Behaviors: Results from Simple Regression Models

Table 7……………………………………………………………………………………….…157 Relationship between WMO and sexual risk behaviors: Results from simple regression models

Table 8……………………………………………………………………………………….…159 Partner Type Moderating the Relationship Between WMS, WMO and Condom Use at Recent Sexual Encounters

Table 9……………………………………………………………………………………….…162 Hypothesis 2.1: Results from Partner Communication Mediation Models Predicting Proportion Condom Use for Vaginal Sex (Past 3 Months)

Table 10……………………………………………………………………………………...…163 Hypothesis 2.1: Results from Partner Communication Mediation Models Predicting Condom Use for Anal Sex (Past 3 Months)

Table 11……………………………………………………………………………………...…164 Hypothesis 2.2: Results from Fear of Condom Negotiation Mediation Models Predicting Proportion Condom Use for Vaginal Sex (Past 3 Months)

Table 12……………………………………………………………………………………...…165 Hypothesis 2.2: Results from Fear of Condom Negotiation Mediation Models Predicting Condom Use for Anal Sex (Past 3 Months)

Table 13……………………………………………………………………………………...…166 Hypothesis 2.3: Results from Peer Norms for Risky Sexual Behavior Mediation Models Predicting Proportion Condom Use for Vaginal Sex (Past 3 Months)

Table 14……………………………………………………………………………………...…167 Hypothesis 2.3: Results from Peer Norms for Risky Sexual Behavior Mediation Models Predicting Condom Use for Anal Sex (Past 3 Months)

Table 15……………………………………………………………………………………...…168 Hypothesis 2.4: Results from Partner Trust Mediation Models Predicting Condom Use Boyfriend or Main Sexual Partner at Most Recent Sexual Encounter

Table 16……………………………………………………………………………………...…169 Hypothesis 2.5: Results from Sex-Related Alcohol Expectancies Mediation Models Predicting Having Sexual Encounters Involving Alcohol (Past 3 Months)

Table 17……………………………………………………………………………………...…170 Results of CFA of proposed measurement model for Attachment

Table 18……………………………………………………………………………………...…172 Results of CFA of proposed measurement model for Affective Attachment and Social Norms

Table 19……………………………………………………………………………………...…174 Results of CFA of proposed measurement model for Sexual Division of Power

Table 20……………………………………………………………………………………...…177 Results of CFA of proposed measurement model for Sexual HIV/STI Risk Behaviors

Table 21……………………………………………………………………………………...…179 Results of CFA of proposed measurement model for Behavioral Risk

Table 22……………………………………………………………………………………...…182 Results of CFA of proposed measurement model for Affective Personal Risk

Table 23……………………………………………………………………………………...…184 Results of CFA of proposed measurement model for Knowledge Based Personal Risk

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Figure Page Figure 1………………………………………………………………………………………145 Bartholomew and Horowitz’s (1991) four-category model of attachment

Figure 2………………………………………………………………………………………146 Conceptual model from DePadilla et al. (2011) for the Theory of Gender and Power as applied to condom use among women

Figure 3…………………………………………………………………………………………147 Model of the Theory of Gender and Power as applied to HIV-risk among women with added Attachment construct

Figure 4…………………………………………………………………………………………148 Structural model of the Theory of Gender and Power as applied to HIV-risk among women with added Attachment construct

Figure 5…………………………………………………………………………………………160 WMS mediation models, controlling for effect of WMO

Figure 6…………………………………………………………………………………………161 WMO mediation models, controlling for effect of WMS

Figure 7…………………………………………………………………………………………171 Attachment latent construct measurement model

Figure 8…………………………………………………………………………………………173 Affective Attachment and Social Norms latent construct measurement model

Figure 9…………………………………………………………………………………………176 Sexual Division of Power latent construct measurement model

Figure 10…………………………………………………………………………………..……178 Sexual HIV/STI Risk Behaviors latent construct measurement model

Figure 11…………………………………………………………………………………..……181 Behavioral Risk latent construct measurement model

Figure 12………………………………………………………………………………..………183 Affective Personal Risk latent construct measurement model

Figure 13………………………………………………………………………………………185 Knowledge Based Personal Risk latent construct measurement model

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Examining the Association between Attachment and Sexual HIV-Risk Behaviors among African

American Young Women

Recent estimates indicate that 23% of the individuals living with human immunodeficiency virus (HIV) in the United States (U.S.) are female (Center for Disease

Control and Prevention (CDC), 2014), with women accounting for approximately 20% of new annual HIV diagnoses (CDC, 2014). The majority (84%) of incident HIV infections among women are due to unprotected heterosexual contact. Heterosexual African American women are disproportionately affected by HIV, as compared to Caucasian and Hispanic/Latina women; heterosexual African American women account for 64% of new HIV infections among women

(CDC, 2015a), but represent only 15.3% of the U.S. female population (United States Census

Bureau, 2014). Young women, particularly young African American women, are also disproportionately affected by sexually transmitted infections (STI), which increase biological susceptibility to HIV (CDC, 2013, 2014, 2015a). For example, rates of incident chlamydia infections are highest for African American women, compared to all other racial/ethnic groups

(CDC, 2014). Gonorrhea prevalence rates are 10.2-13.5 (depending on the corresponding age range) times higher among African American women (CDC, 2014) than Caucasian women; syphilis rates among women are highest for African Americans, with a rate 15 times higher than

Caucasian women (CDC, 2014).

The sexual health disparities experienced by women, particularly young African

American women, are all the more alarming considering that only 45% of HIV–infected women receive HIV-related healthcare (CDC, 2015a). Moreover, a mere 32% of HIV-infected women achieve viral suppression, and accordingly, women account for 24% of yearly AIDS diagnoses

(CDC, 2015a). Thus, African American young women represent a vulnerable, HIV/STI at-risk

population. Efficacious HIV/STI prevention interventions that are gender- and culturally-tailored to young African American women are essential to reduce HIV/STI health disparities among this at-risk population. In order to develop and implement efficacious primary HIV/STI prevention interventions for African American young women, it is important to understand constructs associated with engaging in behaviors that increase HIV/STI risk (e.g., sex without a condom, unprotected sex while intoxicated, and unprotected sex with multiple and/or casual partners).

Sexual behaviors typically take place within the context of an interpersonal sexual interaction between two people. Each individual brings his/her own expectancies of the sexual encounter to the situation (McNulty & Karney, 2004). Sexual expectancies reflect an individual’s beliefs regarding the likelihood of positive or negative consequences resulting from engaging in a particular sexual behavior; for example, the belief that engaging in intercourse will produce feelings of excitement and pleasure (Bourdeau, Grube, Bersamin, & Fisher, 2011).

Expectancies may affect how an individual evaluates and experiences a sexual encounter through several possible mechanisms. Individuals may behave in ways that are consistent with their expectancies, thereby fulfilling them (behavioral confirmation; McNulty & Fisher, 2008). For example, a woman may expect that sex will be exciting and pleasurable, behave in a manner that is conducive to a pleasurable experience, and evaluate the experience as pleasurable (Merton,

1948 as cited in McNulty & Fisher, 2008). Expectancies may also influence one’s interpretation of a sexual encounter, such that interpretations are consistent with prior expectancies (perceptual confirmation; Fiske & Taylor, 1991 as cited in McNulty & Fisher, 2008). Although this may be true, people may also compare the outcome of a situation with prior expectancies; being satisfied with the encounter when it exceeds expectancies, and disappointed when the outcome falls short of prior expectancies (Lawrance & Byers, 1995).

Sexual behaviors and experiences are also influenced by an individual’s sexual motives

(Cooper, Shapiro, & Powers, 1998). Motivations for engaging in sexual encounters are driven by immediate emotional or psychological needs that one believes can be fulfilled by sexual activity

(e.g., having sex will enhance feelings of excitement and pleasure; Cooper, Shapiro, et al.,

1998). Indeed, although behaviors exhibited by different people (such as not using a condom) may outwardly appear similar; these behaviors may serve different purposes for each person, and be motivated by different needs (e.g., expressing love and trust, or avoiding rejection; Cooper et al., 2006). As such, people are motivated to engage in sexual activities for different reasons, and believe sexual activity serves different purposes based on their sexual motives (Cooper, Shapiro, et al., 1998). According to Cooper, Shapiro, and Powers (1998), sexual motives are multidimensional, and account for both (1) the extent to which sexual activity involves the pursuit of pleasure or avoidance of negative/painful emotions and (2) the degree to which the activity is motivated by intra-individual (self-focused: affirming one’s identity, attractiveness, competency) concerns or interpersonal (other-focused: intimacy; pleasing partner; gaining approval of peers or partner) concerns. Thus, people may engage in sexual acts in order to feel closer to or foster intimacy with another person, to experience physical pleasure, to cope with negative affect, to be reassured of desirability or lovability, or to please one’s peers or partner

(Cooper, Shapiro, et al., 1998). Sexual behavior, as viewed from a functionalist perspective

(Snyder & Cantor, 1998), is used by individuals to achieve specific goals, thereby shaping the way a person experiences and expresses sexuality within the context of an interpersonal sexual encounter. The link between goals and functional behavior that underlies sexual motives is further supported by research demonstrating that sexual motives are associated with distinct patterns of sexual experiences and sexual behaviors, particularly risky sexual behaviors

(Browning, Hatfield, Kessler, & Levine, 2000; Cooper, Shapiro, et al., 1998). For example, enhancement motives (i.e., having sex because it feels good, is exciting or adventurous) is associated with more positive feelings about sex, more frequent sex, higher numbers of sexual partners (particularly casual partners), and higher rates of STI (Cooper, Shapiro, et al., 1998).

To extend and investigate the observed relationship between sex motives and sexual behavior, Cooper et al. (2006), considered the role of attachment style as predicting sexual motives. Attachment style is a way to categorize how people approach and navigate interpersonal relationships and exchanges. Attachment style orients an individual’s expectations (of self and other), motivations, emotions and interpretations of others’ feelings/actions within interpersonal interactions (Gentzler & Kerns, 2004). While sexual motives explain some of the observed relationships between attachment style and risky sexual behavior (Cooper et al., 2006), it may be that the particular constellation of cognitions, emotions, motivations and expectations (i.e., attachment style) is a more consistent predictor of sexual behavior across situations.

Attachment Theory: Definition and Development of Attachment

Attachment Theory stems from the ethological and evolutionary perspective that individuals innately possess “the propensity…to make strong affectional bonds to particular others” (Bowlby, 1977, p. 201). According to Bowlby (1969, 1983), infants first form attachment bonds with their primary caregivers. As such, infants seek to be close to their primary caregiver

(proximity maintenance), retreat to caregiver when threatened (safe haven), become distressed when separated from the caregiver (separation distress), and perceive the presence of the caregiver as a form of security that allows the infant to direct his/her attention to exploring the environment (secure base; Bowlby, 1969, 1983). Ainsworth investigated the expression of these behaviors in infants in her seminal research study, the Strange Situation (Ainsworth & Bell,

1970). Ainsworth recognized distinct patterns of behavior based on how infants act in the presence of their primary caregiver; how they responded to being separated from their primary caregiver and left alone with a stranger; and how they responded to being reunited with the primary caregiver. Infants who sought proximity and were easily comforted by their primary caregiver upon her return were classified as having a secure attachment style. Anxious-resistant infants demonstrated ambivalence toward caregivers and were not easily comforted upon being reunited with the caregiver. Avoidance of proximity with their primary caregiver and a lack of distress when separated from their primary caregiver indicate avoidant infants (Ainsworth &

Bell, 1970).

As stated by Bowlby, beginning in infancy, people internalize early experiences and exchanges with their primary caregiver, and form working models of attachment based on: “(a) whether or not the attachment figure is judged to be the sort of person who in general responds to calls for support or protection; [and] (b) whether or not the self if judged to be the sort of person towards whom anyone, and the attachment figure in particular, is likely to respond in a helpful way” (Bowlby, 1973, p. 204). According to Bowlby’s (1973) model, over repeated interactions with a primary caregiver, an infant will formulate a template of how these exchanges will unfold, and the infant will adjust his/her behavior accordingly (e.g., if he/she cries, then someone will come). These formulations of human interaction paradigms are then stored as mental representations of self-other transactions, or what Bowlby (1973) described as working models of self and others. Working models of self and other are cognitive and affective structures composed of memories and learned associations between one’s behavior and the expected behavioral response of others, with a primary function of guiding future attachment-related behavior toward a goal (in the most basic sense, survival, but also navigating and coping with

more modern/adult sources of stress, e.g., work, school, romance; Bowlby 1969,1983; J.A.

Crowell & Treboux, 1995). Theoretically, working models form the lens through which subsequent relationships throughout development are viewed, such that an infant whose primary caregiver does not respond to his/her distress cries may come to see people as unreliable or untrustworthy, and avoid seeking closeness with others due to negative expectations (avoidant).

Similarly, adult attachment researchers (Mikulincer & Shaver, 2003; Mikulincer, Shaver, &

Pereg, 2003) have found that when adults experience negative attachment interactions (those which contradict secure attachment processes) with their partners (e.g., conflict, perceived rejection), they engage in secondary attachment strategies of hyperactivation or deactivation.

Hyperactivation of attachment goals presents as directed attempts to gain the acceptance, love or attention of a partner (similar to the infant who continues to cry in the hopes that someone will eventually respond), whereas deactivation involves detachment from and avoidance of a partner

(or primary caregiver) and an increase in self-reliance (Bowlby, 1980; Mikulincer & Shaver,

2003).

Implications of the enduring developmental effects of insecure attachment styles (e.g., avoidant and anxious-ambivalent) are evident in their association with later social anxiety symptoms in adolescents (Lewis-Morrarty et al., 2015), childhood anxiety (Dallaire & Weinraub,

2007), dysfunctional emotional regulation (Moutsiana et al., 2014), and altered brain physiology

(e.g., larger amygdala volumes in later adulthood; Moutsiana et al., 2015). Importantly, attachment is not merely the product of how sensitive and responsive a caregiver is toward an infant. Other factors such as the infant’s temperament, developmental environment, and gene- environment interactions can influence attachment orientations and the effects they bear on development (Gervai, 2009). For example, Dallaire and Weinraub (2007) found that first-graders

who were classified as insecurely attached as 15-month olds, and who had experienced stressful life events (e.g., parents divorced, a parent spent time in jail, parents had financial problems), exhibited more anxiety symptoms than securely attached children who had similar levels of stressful life experiences. A review of the attachment literature suggests that infant temperament

(i.e., disposition for distress, negative emotionality, irritability and soothability) may have an effect on attachment that is moderated by maternal and social variables (i.e., how a mother responds to an irritable infant; Mangelsdorf & Frosch, 1999). In this sense, attachment is the product of the interaction between what the infant and mother both bring to the dynamic.

Furthermore, research indicates that biological factors may also impact attachment. A particular

(short) allele (i.e., segment of genetic coding) is associated with lower serotonin metabolism and later behavioral problems in Rhesus monkeys that were not reared by their mothers, whereas monkeys without that allele (i.e., the long allele) showed normal serotonin metabolism and behavior, regardless of rearing conditions (Suomi, 2006). Similarly, another study demonstrated that a mother’s responsiveness moderated attachment security for human infants with the short allele such that higher levels of responsiveness were associated with secure attachment; however, responsiveness did not moderate attachment security for infants with the long allele (Barry,

Kochanska, & Philibert, 2008). In summary, attachment processes begin to unfold and working models take shape in early infancy, however, these mental representations of self and other are complex and influenced by a number of factors (e.g., temperament, gene-environment interactions, experiences and relationships) throughout development.

Adult Attachment

As individuals progress through development, primary caregivers are no longer the most salient attachment figure. While childhood primary caregivers may remain important in

adulthood, their position as the central object of attachment-related behaviors and thoughts

(proximity maintenance, safe haven, separation distress, secure base) is distributed across other relationships such as friendships and romantic/sexual partnerships. There is mixed evidence regarding the stability of attachment to one’s primary caregiver from childhood into adulthood

(i.e., whether childhood attachment to primary caregiver predicts adulthood attachment to that same individual; Fraley, Vicary, Chloe Brumbaugh, & Roisman, 2011), and also the consistency between attachment to primary caregiver and adult attachment to friends or significant others

(i.e., does childhood attachment to primary caregiver predict attachment to friends and romantic partners in adulthood; Haydon, Collins, Salvatore, Simpson, & Roisman, 2012). Nonetheless, adult attachments to significant others differ from infant-caregiver attachments in that they are more reciprocal in nature, and there is a sexual aspect of the relationship (Hazan, Shaver, &

Bragshaw, 1988). As a result, Hazan et al. (1988) expanded Bowlby’s model of attachment to conceptualize romantic love in adulthood as an attachment process; love being the product of the integration of attachment, caregiving, and sexual systems. Caregiving refers to sensitivity and responsiveness to another individual’s attachment needs (e.g., providing comfort, assurance, encouragement; Bowlby, 1969, 1982). In parent-child relationships, it is the parents’ caregiving system that is responsive to the child’s attachment system. In adult romantic relationships, each individual is a mutual caregiver for the attached other (Mikulincer, 2006). The sexual system includes reproductive efforts as well as the psychological functions that sexual activity can serve

(e.g., sexual motives as described by Cooper, Shapiro, et al., 1998). Hazan et al. (1988) argue that because attachment is focused on self-protection and emerges earlier in development, it affects the other two systems within the context of adult romantic love. According to Bowlby

(1969, 1980), people can only direct their attention toward providing care for others when they

feel secure in their own needs being met. That is, insecure attachment (anxious, avoidant) interferes with one’s ability to provide care for a partner. Similarly, attachment affects sexual motives, experiences and behaviors within the context of adult relationships, such that secure individuals tend to engage in sex in order to express love to their partners, and they experience more positive emotions during sex than insecure individuals (Tracy, Shaver, Albino, & Cooper,

2003); avoidant individuals are less likely to enjoy sex and more likely to report having unwanted but consensual sex (Gentzler & Kerns, 2004); anxious individuals use sex to avoid feelings of abandonment and to bolster reassurance of partner’s interest (Tracy et al., 2003). The complexity of adult romantic relationships is founded (1) by attachment affecting caregiving and sexual systems on an individual level; (2) by attachment, caregiving and sexual systems being exchanged between individuals; and (3) this exchange having the potential to shape, reinforce or deactivate pre-existing assumptions about self and others (working models). Changes in an individual’s social environment, meaningful experiences, and the interactions between her working model of self (as being a caregiver and someone to be cared for) and other (as providing and requiring care) and the working models (self and other) of others close to her may alter or shape her internal working models from infancy/childhood (revisionist perspective; Fraley et al.,

2011).

In summary, as people progress throughout development, attachment bonds-similar though not necessarily of the same nature as those formed in infancy with primary caregivers- are established with friends and romantic/sexual partners. Adult attachment bonds are both interpersonal and intraindividual in the sense that they involve each individual’s perceptions of self and others. Similarly, sexual interactions and exchanges involve two people- each of whom bring with them their own expectations, cognitions, and emotions about self and other (i.e.,

attachment style) to the sexual exchange. Notably, few studies investigating associations between attachment and sexual behavior have operationalized attachment in terms of working model-self and working model-other (Ciesla, Roberts, & Hewitt, 2004; Olley, 2010 being exceptions). That is to say, the vast majority of the existing literature on the association between attachment style and sexual behavior approaches the operational definition of attachment from the categorical perspective- attachment is discussed as either secure, anxious, avoidant, preoccupied, fearful, or dismissing. The following provides a review of the various operationalizations of attachment in the literature on the association between attachment and sexual behaviors. Further, the following also presents the rationale for operationalizing attachment as working model-self (WMS) and working model-other (WMO) in the current study.

Categorization of adult attachment styles. Adult attachment has been classified or categorized in various ways by attachment theorists and researchers. The scope of this review is to highlight the attachment categorizations used in sexual behavior research. Adult attachment has been assessed via various self-report measures based on different classification models of attachment styles. Generally speaking, there are two main categorization schemes for adult attachment; discrete, mutually exclusive categories (typically three-categories), and multidimensional categories reflecting combinations of continuous measures of each dimension, which will be referred to as four-category models. The following sections include: (a) an overview of the three-category model of attachment and associated assessments; (b) limitations of the three-category model; (c) the four-category model of attachment and associated assessments; and (d) the advantages of the four-category model.

Three-category models of adult attachment and associated assessments. Earlier measures of adult attachment were based on a three-category model that incorporated concepts

from child or infant attachment measures. The Adult Attachment Interview (AAI) was the first psychometrically validated measure to assess and categorize adult attachment styles (C. George et al., 2000; Hesse, 2008). The AAI measures adult attachment via assessment of recollections of childhood (Hesse, 2008). Attachment styles are based on the coherence, consistency, and content of the interview. Securely attached adults’ recollections are coherent, consistent, and contain both positive and negative recollections; secure adults are autonomous, open to discussing the past, and value and acknowledge the manner in which previous experiences with a primary caregiver have altered his/her development. Dismissing adults exhibit incoherence by both idealizing and degrading primary caregivers; they have difficulty recollecting memories from childhood, describe themselves as being strong and independent with early experiences of rejection, but may deny or minimize the impact of negative experiences. Preoccupied adults express anger or ambivalence toward primary caregivers; their recollections of the past are inconsistent; and they may engage in parent-blaming (Hesse, 2008). By design, the AAI functions to determine adult attachment to the primary caregiver from infancy, based on adult recollections of experiences with and characteristics of the primary caregiver. By extension, the consistency between infant/childhood attachment to one’s primary caregiver and attachment to that same individual during adulthood may be examined using this instrument. While the consistency between infant attachment and attachment to primary caregivers in adulthood continues to be studied by attachment researchers, others have shifted their focus toward adult attachment within romantic partnerships or other close relationships (i.e., friendships). One reason for such a shift is that adults’ primary attachment figures may not be their primary caregivers from infancy and childhood; rather romantic partners or close friends may come to be viewed as primary attachment figures or potential attachment figures.

Hazan & Shaver (1987) developed a self-report questionnaire to specifically assess romantic attachment in adulthood. The rationale for developing this scale was not to establish the temporal stability of attachment to primary caregivers, but rather to show that similar attachment processes are involved in the context of close romantic relationships. This three-item scale was developed by adapting Ainsworth, Blehai, Waters, and Wall’s (1978) descriptions of infant attachment styles so that they may apply to adult love. In its original form, individuals picked which one of the three items most accurately describes them in romantic relationships (later forms allow participants to rate each item in terms of how characteristic of them it is, and then categorizes individuals according to the item with the highest rating). The secure item reflects a self-characterization as comfortable with intimacy, dependency, and reciprocity in relationships, with low levels of loss or abandonment anxiety (J.A. Crowell & Treboux, 1995; Hazan &

Shaver, 1987). For the insecure attachment styles, anxious/ambivalent: describes a desire to be close to others, anxiety about rejection, and self-awareness that the individual desires more intimacy than most people (J.A. Crowell & Treboux, 1995; Hazan & Shaver, 1987); avoidant: is characterized by lack of trust, and discomfort with intimacy, closeness, and dependency (J.A.

Crowell & Treboux, 1995; Hazan & Shaver, 1987).This three-item scale was embedded within a

95-item questionnaire (based on existing love questionnaires and infant-caregiver attachment measures) to demonstrate that the adult romantic attachment styles predict the way that individuals experience love. Accordingly, findings from this measure suggested that secure individuals describe love experiences as happy, friendly and trusting; avoidant as provoking fear of intimacy, fear of jealously, and fear of highly emotional experiences (both positive and negative); and anxious experience love as obsessive, highly emotional (both positive and negative), and provoking one’s own jealousy (Hazan & Shaver, 1987). Analysis of the 95-item

questionnaire also demonstrated that romantic attachment is related to working models of romantic relationships (i.e., “how the individual views the course of romantic love over time”;

Hazan & Shaver, 1987, p. 515). Furthermore, past experiences with parents, perceived quality of relationship with parent, and parents’ relationships with each other predicted romantic attachment style (Hazan & Shaver, 1987). While this framework of adult romantic attachment highlights the notion that processes similar to those involved in infant attachment are also present in adult romantic attachment, there are several disadvantages of the three-category model of attachment in adult romantic relationships.

As Bartholomew and Horowitz (1991) noted, categorizing adult attachment as either secure, anxious or avoidant does not adequately conceptualize people who have high levels of both dependency and avoidance. Further, it is not sensitive to individuals who may endorse some characteristics of a particular attachment style but not all of them. Collins and Read (1990) attempted to improve the discrete three-category model based on the argument that each category contains statements about multiple aspects of relationships (i.e., the secure description describes being comfortable with closeness and being able to depend on others), thereby forcing participants to endorse descriptions that may or may not be entirely reflective of their tendencies regarding all of the aspects included (Collins & Read, 1990). The Adult Attachment Scale

(AAS; Collins & Read, 1990) is based on a factor analysis of Hazan and Shaver’s (1987) categorical model. The AAS consists of three subscales or dimensions as opposed to distinct categories. The anxiety subscale is a measure of anxiety over being abandoned or unloved; close subscale is an index of level of comfort with closeness; and depend reflects ability to depend on others. Each individual receives a score on each dimension. These three dimensions are theoretically and empirically related to Bartholomew and Horowitz’s (1991) working model-self

and –other (see next section on four-category models of attachment), however Bartholomew and

Horowitz’s (1991) model is more comprehensive in that incorporates different combinations of the dimensions of working model-self and –other, and describes how these combinations are reflective of four categories of attachment.

Summary. Attempts to measure and categorize adult attachment initially sought to apply the model of infant attachment to adult attachment, with the goal of determining the temporal continuity of attachment to a primary caregiver from infancy to adulthood. As such, the development of psychometric instruments to measure adult attachment began with a three- category model adapted from the categorization of infant attachment. Instruments that followed were designed to determine if attachment to romantic partners or other close relationships in adulthood could be conceptualized similar to the way attachment to a primary caregiver in infancy is conceptualized; this approach also used a three-category model to assess adult attachment style. Attachment theorists have since noted that there are disadvantages to conceptualizing adult attachment using terminology adapted from models of infant attachment.

Four-category models of adult attachment and associated assessment. As noted in a previous section, while adult attachment may share similarities with infant attachment to a primary caregiver, adult attachment (e.g., to romantic partners, to caregivers from childhood, and to other important peers) is more reciprocal and dynamic. For this reason, models of adult attachment may be more complex than models of infant attachment. As such, more recent conceptualizations of adult attachment are based on a multidimensional approach that yields four-categories of adult attachment.

Bartholomew and Horowitz (1991) challenged the notion that there are three adult attachment styles. They also pointed out that there are inconsistencies between how different

measures conceptualize the same attachment style. For example, the AAI described avoidant individuals as denying distress and the importance of attachment relationships, whereas Hazan and Shaver’s (1987) model conceptualized avoidant individuals as experiencing distress when becoming close with others. Bartholomew and Horowitz suggested that these categories may

“obscure conceptually separable patterns of avoidance in adulthood” (1991, p. 227) by not differentiating between individuals who desire to become close with others but for some reason have difficulty doing so, and individuals who lack a desire to become close with others altogether. They suggested that there are actually four categories of attachment, based on combinations of working models of-self (a continuous measure of the degree to which one views oneself as lovable and worthy of acceptance) and -other (a continuous measure of the degree to which one views others as reliable, trustworthy, and accepting). These working models, like

Bowlby’s (1969,1980) working models, are cognitive constructs that are prototypes or mental representations of relationships based on previous interactions and experiences. These mental representations include thoughts about the self (e.g., roles, behaviors, personal characteristics such as likeability and self-worthiness) and others (e.g., how others will respond to oneself, how reliable and trustworthy others are, and the value of others’ acceptance and approval of oneself) in relationships (J.A. Crowell & Treboux, 1995). Working models serve as frameworks that guide behavior in relationships, expectations about relationships, behavioral strategies within relationships, and future relationships (J.A. Crowell & Treboux, 1995). J.A. Crowell and

Treboux (1995) point out that these cognitive constructs are called “working” models because although they guide behavior in relationships, they may be revised by significant attachment- related experiences with new or established attachment figures. Similarly, Bevington, Fuggle, and Fonagy (2015), suggest that mental representations of self and other may also be altered

through the development of a secure attachment bond with a therapist. Kobak, Zajac, Herres,and

Ewing (2015) also suggest that internal working models may be altered by therapeutic techniques and the therapeutic alliance itself. This is a particularly important point with regard to the potential utility of targeting working models, in the event that working models of-self and - other predict engagement in risky sexual behavior as well as risk factors for engagement in risky sexual behavior. Within Bartholomew and Horowitz’s four-category model of attachment, the four categories are the product of various combinations of the dimensions of working model-self and –other. As depicted in Figure 1, secure views self as lovable, and others as accepting and responsive- they have a sense that they are good (working model-self) and others are good as well (working model-other), however they do not depend on the acceptance of others in order to validate their perceptions of themselves as good. Preoccupied views self as unlovable (a less positive working model-self as compared to secure individuals), and others as being sources of validation (i.e., self-approval is obtained by being accepted by others). Fearful individuals view self as unlovable (a less positive working model-self as compared to secure individuals), and others as rejecting, untrustworthy and unreliable (less positive working model-other as compared to secure individuals). Dismissive see themselves as valuable, and internally validate their own self-worth (a working model-self similar to secure individuals), and others as untrustworthy and unreliable (a less positive working model-other as compared to secure individuals; Bartholomew

& Horowitz, 1991) (see Figure 1). Dismissing and fearful styles are similar in that both avoid intimacy; however, they are different in that fearful requires the acceptance of others in order to establish a positive sense of self. Fearful is similar to preoccupied in that both rely on the acceptance of others to validate sense of self. However, fearful and preoccupied differ in their

willingness to get involved in close relationships; preoccupied approaches others whereas fearful is reluctant and wary of disappointment (Bartholomew & Horowitz, 1991).

The attachment categories secure, preoccupied, dismissive, and fearful provide more information than secure, anxious or avoidant. When an individual is described as fearful- this is interpreted to mean that that individual has a negative working model of self (e.g., “I am unlovable”), a negative working model of others (e.g., “Others are untrustworthy and unaccepting”), requires acceptance from others to foster a positive self-regard, is reluctant to get close with others for fear of rejection, has a high level of anxiety within relationships, and also has a high level of avoidance in relationships. This description is richer and more nuanced than the singular descriptor “anxious” or “avoidant.”

Overall, the four-category model allows for a more flexible and informative measure of adult attachment. Instead of being one attachment style or another, an individual can have varying degrees of attachment orientations (what would have been discrete categories in the three-category model). By allowing for and measuring inter-individual degrees of different attachment orientations, the four-category model is more informative than the three-category model.

Additionally, two key constructs, working-model self and working model-other, can be derived from the four-category model. The Relationship Questionnaire (RQ) reflects

Bartholomew and Horowitz’s (1991) four -category model of adult attachment. In the current version of the RQ, participants rate how characteristic the description of each attachment style is of them. These four scores can be combined to compute indices of working model-self

[calculated as (secure+dismissive)-(preoccupied+fearful)] and working model-other [calculated as (secure+preoccupied)-(dismissive+fearful)]. Higher working model-self scores indicate more

positive self-regard, and higher working model-other scores indicate more positive other-regard

(Griffin & Bartholomew, 1994). Collins and Read’s (1990) subscales conceptually and empirically overlap with these working models in that they are associated with mental models of self and the social world (Collins & Read, 1990); however, as noted in a previous section, The

AAS (Collins & Read, 1990) does not synthesize these dimensions into attachment categories based on all of the possible combinations of working model-self (as more or less good) and working model-other (as more or less good).

The Experiences in Close Relationships (ECR) questionnaire ( Fraley, Waller, &

Brennan, 2000), is based on a model that is similar to and corresponds with Bartholomew and

Horowitz’s (1991) model; however, the dimensions in Fraley et al.’s (2000) model are (1) level of anxiety about relationships and (2) level of avoidance in relationships. According to Fraley et al. (2000), the dimension of attachment anxiety in Fraley et al.’s model corresponds with

Bartholomew and Horowitz’s (1991) working model-self dimension; likewise, attachment avoidance corresponds with working-model other.

In addition to providing more discreet indices of attachment in general, the four-category models of attachment may be more sensitive to gendered effects of attachment on sexual behavior. This is particularly useful given the overall aim in the current study is to examine the association between adult attachment and risky sexual behaviors among African American women.

Advantages of the four-category model of adult attachment: Gender implications.

Although Attachment Theory does not suggest that there are gender differences in attachment styles, adult heterosexual romantic relationships and sexual behaviors are gendered, and the sexual expression of attachment anxiety and avoidance may be influenced by cultural gender-

norms of sexual and emotional expression. Indeed, several researchers have found gender differences in the expression of attachment style, particularly with regard to sexual behaviors

(Cooper et al., 2006; Monteoliva, García-Martínez, Calvo-Salguero, & Aguilar-Luzón, 2012).

Gender differences in the expression of attachment anxiety suggest that females with high levels of attachment anxiety are more likely to engage in certain risky sexual behaviors, whereas males with high levels of attachment anxiety may be less likely to engage in those risky sexual behaviors (Cooper et al., 2006; Monteoliva et al., 2012). Among men and women who endorse a fearful attachment style (high levels of anxiety and avoidance), the presence of high levels of attachment anxiety may have a stronger impact on women’s sexual behavior than on men’s (i.e., lead to more risky sexual behaviors among females); whereas the co-occurring high levels of avoidance may have a stronger effect on males’ sexual behaviors, and perhaps act as a buffer against engagement in risky sexual behavior among fearful men (Cooper et al., 2006). For, example, Schmitt and Jonason (2014) found that fearful attachment was not associated with sexual permissiveness (e.g., tendency to have one-night-stands, cheat on a sexual partner) among men; however, fearful attachment was significantly associated with more sexual permissiveness among women. These effects may be present due to sociocultural conventions and expectations that differentially impact the saliency of attachment anxiety and avoidance with regard to sexual behaviors among males and females. Using the four-category model to assess the relationship between attachment style, gender and risky sexual behavior, Cooper et al. (2006) found that stereotypical gender-norms may mitigate or enhance the effects of attachment insecurity on sexual behavior. Specifically, among women, gender-stereotypes of women as nurturing, warm and expressive may temper the effects of attachment avoidance. Likewise, the feminine stereotype of women as being needy, overly emotional, and dependent on men may exacerbate

the effects of attachment anxiety on sexual behaviors among females (Cooper et al., 2006).

Cooper et al. (2006) suggest that male gender-roles that encourage independence and self- reliance may mitigate the effects of attachment anxiety; while gender stereotypes of men as minimizing intimacy and emotional vulnerability may exacerbate the effects of attachment avoidance. This and other research indicate that the sexual behaviors associated with attachment anxiety and avoidance may be influenced by gender-based norms and stereotypes (see section on

Associations between Attachment Style & Risky Sexual Behavior). Since the four-category models of attachment provide a measure of attachment that accounts for levels of both attachment anxiety and avoidance, and since attachment anxiety and avoidance might differentially impact men and women’s sexual behaviors, the four category models of attachment may provide a means for refining our understanding of gender differences in the sexual behaviors associated with particular attachment styles.

Research indicates that there are also gender differences in the association between the attachment dimensions working model-self and working model-other and sexual behavior.

Schmitt and Jonason (2014) found that a higher working model-self score was associated with previous infidelity among men; however, a lower working model-self score was associated with previous infidelity among women. Furthermore, Schmitt and Jonason (2014) found that a lower working model-self score was associated with short-term mating strategies (i.e., preference for casual sex) among women, but not men; whereas a lower working model-other score was associated with a preference for casual sex among men, but not among women. These findings suggest that women’s engagement in certain sexual behaviors may be affected by a lower working model-self score; however, whether a lower working model-self is associated with other risky sexual behaviors of interest to the current study has not yet been determined.

Literature regarding gender differences in the association between internal working models and sexual behavior suggests that these differences may reflect that gender is a social construction that is integrated as an aspect of an individual’s working model-self and –other

(Hart, 1996). According to Chodorow, gender is “processual, reflexive, and constructed”

(Chodorow, 1989, p. 113); notably, the same could be said about internal working models of self and others (Gergen, 1977). Self and other concepts are informed by gendered social interactions, gender stereotypes of social roles, and gender-related expectations (Cross & Madson, 1997).

Traditional gender roles tend to place men in a position of power and independence, and women in a position in which they are dependent upon males (Wingood & DiClemente, 2000). These pervasive attitudes toward gender roles are internalized by men and women from an early age.

Indeed, Hart (1996) argues that the gendered aspects of self and other that are incorporated into working models perpetuate gendered relations and power differentials; moreover, literature suggests that gender differences in self-concept may account for gender differences in cognition, motivation, emotion and social behaviors (Cross & Madson, 1997). A review of gender differences in self-concepts suggests that females’ mental representations of self are more likely to be interdependent, meaning they are based on group membership and the quality of one’s relationships with others; whereas, males’ self-concepts are independent (i.e., based on one’s unique abilities, and the importance of distinguishing oneself from others; Cross & Madson,

1997).

It has been suggested that the gender-related aspects of working models of self and other are internalized as beliefs about what men and women should do, and the different characteristics attributed to each gender (Hart, 1996). This likely extends to ideas about how men and women should behave and interact within sexual encounters. Being that women may be more likely to

have interdependent concepts of self and other, a proportion of women may have a tendency to hold sexual partners (others) in high regard, and look to these sexual partners for approval or validation. Whether a higher working model-other is associated with engagement in particular risky sexual behaviors among females remains to be determined and will be examined within the current study.

Summary. Both Bartholomew and Horowitz’s (1991) and Fraley et al.’s (2000) multidimensional, four-category models of attachment highlight the effects gender may have on the strength and nature of the relationship between attachment dimensions (working model-self, working model-other, anxiety, avoidance) and sexual behaviors. Being that the current study focuses on sexual HIV-risk behaviors among African American females, the four-category model of attachment is ideal for identifying the attachment-related constructs (i.e., dimensions within a working model conceptualization) that are associated with sexual behaviors among females in particular.

Operationalization of attachment style in the current study. The current study uses the

Relationship Questionnaire (RQ) to measure attachment according to Bartholomew and

Horowitz’s (1991) four-category model of attachment. As this study focuses on African

American young women’s sexual behaviors, this model may help highlight gendered expressions of attachment that are unique to females. Moreover, Bartholomew and Horowitz’s model can be used to formulate working model-self and working model-other scores. The current study focuses on the associations between these dimensions of attachment (i.e., working model-self and –other) and sexual behaviors because sexual behaviors occur within the context of an interpersonal, dynamic exchange between each participant’s concept of self and other (Cooper, Shapiro, et al.,

1998). Attachment (Bowlby, 1977) and social constructivist theorists (Benhahih, 1987;

Benjamin, 2013; Hart, 1996) agree that the concept of “self” is the product of human social and emotional exchanges, and it is created in relation to others. As previously noted, conceptions of self and other are gendered (Hart, 1996), and color how an individual interprets and experiences future social interactions. Being that heterosexual behaviors among females are also gendered and occur within a specific type of human social and emotion exchange, working models of –self and -other may play a particular role in these behaviors.

While attachment anxiety and avoidance may be influenced by gender norms, they do not include internalizations of self as being a member of a particular gender- i.e., what being a female means within the context of a sexual encounter with a male- in the manner that working model-self and –other may. Moreover, the dimensions of attachment anxiety and avoidance represent attachment patterns or styles, whereas the dimensions of working model-self and – other reflect internal representations that guide outward displays of attachment patterns (or combinations of attachment patterns; Bowlby, 1977; Pietromonaco & Barrett, 2000). As such, the dimensions of working model-self and working model-other may represent core, schema-like psychosocial constructs that affect cognitions, emotions and behaviors within sexual relationships, while attachment avoidance and anxiety represent the (sexual) relational behavioral patterns in and of themselves. Furthermore, given working models are measurable and potentially changeable cognitive structures (Collins & Read, 1990; J.A. Crowell & Treboux,

1995 ; Pietromonaco & Barrett, 2000), these indices (working model-self and –other) may represent constructs that HIV prevention interventions could assess to identify women who may be less responsive to HIV prevention intervention efforts, and require preliminary interventions that target these concepts of self and other before targeting risk factors associated with discrete

sexual behaviors. Therefore, the current study focuses on the relationship between the attachment constructs of working model-self and -other and sexual behaviors that may increase risk for HIV.

Associations between Attachment Style & Risky Sexual Behavior

The following is a review of the existing literature on the association between attachment style and sexual behaviors that confer increased risk of contracting HIV or STI (e.g., infrequent or inconsistent condom use, casual sexual partners, higher number of sexual partners, and consuming alcohol prior to engaging in sexual encounters). The extant literature indicates that securely attached individuals are more likely than their insecure (classified as anxious, avoidant, preoccupied, fearful, or dismissive depending on study categorization of attachment) counterparts to have sex within the context of a committed relationship (Brennan & Shaver,

1995; J.A. Feeney, Noller, & Patty, 1993; Hazan, Zeifman, & Middleton, 1994 as cited in

Cooper et al., 2006); less likely to have casual sex partners, one-night-stands, or to engage in sexual acts with a person other than one’s primary partner (Hazan et al., 1994; Paul, McManus,

& Hayes, 2000); and less likely to have had sex with a stranger or be the perpetrator or victim of sexual aggression (Cooper, Shaver, & Collins, 1998; Tracy et al., 2003). Although, securely attached women are less likely to use condoms, this likely reflects the tendency of secure individuals to have sex within the confines of a committed steady relationship (Bogaert &

Sadava, 2002) that has established condom-use norms, with most couples preferring to not use condoms (Chenglong et al., 2011).

As the literature on secure individuals’ patterns of sexual behaviors reflects less sexual risk taking in general, the remainder of the review will focus on sexual risk taking among individuals labeled as insecure (preoccupied, fearful, dismissive, anxious, avoidant). Of note, the majority of the reviewed literature employs the discrete categorical approach to attachment. Only

two studies report the association between working model-self, working model-other and sexual behavior (Ciesla et al., 2004; Olley, 2010). The current study will fill this gap in the literature by assessing the association between the attachment-related constructs working model-self, –other and risky sexual behavior.

Association between attachment style and condom use. Infrequent or inconsistent condom use can increase risk of contracting or spreading HIV/STI (CDC, 2015b). The CDC recommends consistent and correct condom use as an effective strategy for preventing transmitting HIV to a sexual partner, and protecting oneself from contracting HIV (CDC,

2015b). Nonetheless, cross-sectional studies indicate that African American young women do not use condoms consistently (Wingood & DiClemente, 1998a), and the most common HIV transmission route among African American young women is via unprotected sex with HIV- infected males (CDC, 2015a). In what follows, studies examining the association between attachment style and condom use are reviewed.

Anxiety about relationships (a characteristic of the anxious style and dimension of attachment) has been associated with greater frequency of unprotected sex, inconsistent condom use (over an eight-week period), and negative attitudes toward condoms among both males and females (J.A. Feeney, Peterson, Gallois, & Terry, 2000). In contrast, Bogaert and Sadava (2002) found that the anxious attachment style was cross-sectionally associated with more consistent condom use among young adult females. However, in this study, participants reported on a single-item assessing condom use over the previous year. The methodology of J.A. Feeney et al.,

(2000) included the utilization of several items to assess condom use over a discrete and recent period of time, and may more accurately reflect recent condom usage patterns. Furthermore, as noted by the Bogaert and Sadava (2002), the positive association between anxious attachment

and condom use among their sample of women was likely driven by their reported tendency to have a higher number of sexual partners. In line with the J.A. Feeney et al. (2000) findings, most studies that assess recent condom use over a discrete time period (e.g., 30 days; Olley, 2010) among predominantly or exclusively female samples indicate that anxious attachment is associated with decreased condom use (Kershaw et al., 2007; Olley, 2010; Strachman & Impett,

2009).

Research indicates that avoidant individuals or individuals with high levels of attachment avoidance (e.g., dismissive) approach condom use differently than anxious individuals. Cooper et al., (2006) note that avoidant individuals may avoid sexual intimacy and closeness in sexual relationships by having sex within contexts that preclude the development of true intimacy

(multiple casual sexual partners). Avoidant individuals may also minimize intimacy by using condoms as a means to “ensure that the relationship does not evolve into a committed one”

(Cooper et al., 2006, p. 249). Avoidant males in particular (but not avoidant females) are likely to report that they used condoms every time they have sex over an eight-week period (J.A.

Feeney et al., 2000), which may be due to the moderating effect of gender on the expression of avoidance and the association between attachment and sexual behaviors (Monteoliva et al., 2012;

Sprecher, 1985). For example, dismissive men report more negative attitudes toward expressing one’s feelings than preoccupied or fearful men, while dismissive women do not differ from preoccupied or fearful women with regard to their feelings toward expressing their emotions

(Monteoliva et al., 2012). Furthermore, a meta-analysis of sex differences in attachment styles indicates that men show more avoidance and lower anxiety than females (Del Giudice, 2011), which may help explain why even highly avoidant, dismissive females evince patterns of behavior associated with attachment anxiety. Further, although research indicates that women

who endorse a discomfort with closeness in relationships (a characteristic of avoidance) reject the belief that condoms are boring (J.A. Feeney et al., 2000), it is unclear whether this belief is associated with condom use behaviors.

There is less evidence regarding the condom use behaviors of individuals with high levels of both attachment anxiety and avoidance relative to the condom use patterns of individuals with high levels of anxiety-only or avoidance-only. Strachman and Impett (2009) found a significant difference in condom use between anxious and avoidant individuals, such that avoidant individuals were more likely than anxious individuals to use condoms on a daily basis over the course of two weeks. Being that fearful attachment represents high levels of both anxiety and avoidance, it may present with a mixture of characteristics from anxious and avoidant attachment orientations (B.C. Feeney & Kirkpatrick, 1996; Mikulincer & Florian, 1999 as cited by Kershaw et al., 2007). This may manifest in inconsistent condom use depending on interpersonal and environmental contexts, or it may result in intentions to use condoms that do not translate into actual condom use behaviors. As noted above, gender-norms and stereotypes may also influence the expression of attachment anxiety and avoidance among females, such that anxiety may have a stronger effect on sexual behaviors than avoidance does among females with high levels of both attachment anxiety and avoidance (i.e., fearful). Notably, Olley (2010) found that negative working model-self and fearful attachment style were associated with the number of times a condom was not used during intercourse in the past three months among HIV-infected women in Nigeria.

To summarize, extant literature on the relationship between adult attachment and condom use suggests that attachment anxiety is associated with less consistent condom use, and more frequent unprotected sex, particularly among females. Attachment avoidance is associated with

more frequent or consistent condom use among men, however, the relationship among women is less clear. Being that there is only one study on the association between condom use and fearful attachment (high anxiety and high avoidance), the relationship between condom use and fearful attachment among females remains inconclusive. Findings from Olley (2010) suggesting that fearful attachment is associated with a lower likelihood of condom use, highlights the possibility that gender-norms/stereotypes may affect the expression of concomitant attachment avoidance and anxiety. Olley (2010) also found that a negative working model-self was associated with a lower likelihood of condom use. It may be that this aspect of fearful attachment is driving the association between fearful attachment and a reduced likelihood of using condoms. Being that preoccupied attachment presents similarly to traditional conceptions of anxious attachment, preoccupied attachment may also be associated with a reduced likelihood of using condoms.

Furthermore, as preoccupied attachment is associated with a negative working model-self and a positive working model-other, perhaps a positive working model-other contributes to a tendency to have unprotected sex.

Association between attachment and number of sexual partners. Higher numbers of sexual partners increases the likelihood of having sex with an HIV-infected individual or an individual with an STI (CDC, 2015b). The CDC recommends reducing number of sexual partners to minimize risk of contracting or transmitting HIV (CDC, 2015b). The literature regarding the association between attachment and number of sexual partners is reviewed.

Evidence suggests that anxious attachment style is cross-sectionally associated with reporting multiple sexual partners (> two male partners in the past year; Ahrens, Ciechanowski,

& Katon, 2012), higher number of lifetime partners (Bogaert & Sadava, 2002), and is correlated with a higher number of lifetime extra-pair sexual partners (sexual partners other than the

individual with whom a women was/is in a relationship; Gangestad & Thornhill, 1997). Fearful attachment and negative working model-self are also associated with a higher numbers of sexual partners in the past three months (Ciesla et al., 2004), including among HIV-infected women in

Nigeria (Olley, 2010). Furthermore, speaking to gender differences in attachment-related sexual behaviors, avoidant females and anxious males are the least likely to report engaging in sexual intercourse during the course of a six-week period (J.A. Feeney et al., 1993), and avoidant attachment predicts a lower number of lifetime extra-pair sexual partners among women, but a higher number of lifetime extra-pair sexual partners among men (Gangestad & Thornhill, 1997).

Overall, research indicates that attachment anxiety is associated with a higher number of sexual partners, while attachment avoidance is associated with a lower number of sexual partners. Preoccupied attachment corresponds with traditionally described anxious attachment presentations; since preoccupied attachment corresponds with a negative working model-self and a positive working model-other, a negative working model-self and a positive working model- other may be associated with a higher number of sexual partners. Two studies suggest that elevated anxiety and avoidance (i.e., fearful attachment) among females is also associated with a higher number of sexual partners. It is possible that a negative working model-self may underlie the association between fearful attachment and a higher number of sexual partners among women, as research indicates that a lower working model-other may not be associated with engaging in risky sexual behavior among women (Schmitt & Jonason, 2014).

Association between attachment and partner type and the interaction between attachment and partner type on condom use behaviors. Engaging in sexual intercourse with casual partners may decrease the likelihood of knowing the partner’s sexual history, HIV serostatus, or STI history (Parsons et al., 2005). As a result, individuals engaging in sex with

casual partners may not take the necessary precautions to prevent HIV/STI acquisition (Mutchler et al., 2008). Attachment style may be associated with increased vulnerability for contracting

HIV via engagement in unprotected sex with casual partners, particularly among preoccupied or fearful women, who may be less likely to use condoms in general.

Cross-sectional studies indicate that attachment anxiety among women is associated with:

(a) increased odds of having sex without knowing a partner’s history (Ahrens et al., 2012; J.A.

Feeney et al., 2000); (b) having multiple male partners and an STI history (Ahrens et al., 2012),

(c) greater sexual unrestrictiveness (i.e., overall number of sexual partners and number of partners only had sex with one time; Sprecher, 2013); and (d) negatively associated with lifetime history of being in a sexually exclusive relationship (J. A. Feeney et al., 2000). Based on these findings, anxious attachment is associated with having more casual sexual partners (as opposed to steady or committed partners), and a lower likelihood of using condoms within the context of sexual encounters with casual partners. Avoidant females are more likely to have sex outside the context of a steady partnership over one’s lifetime (Gentzler & Kerns, 2004), and avoidant young adults are more likely than their secure counterparts to have had one-night-stands and sexual encounters with a stranger (Cooper, Shaver, et al., 1998; Paul et al., 2000). Aside from

Gentzler and Kerns (2004), research specifically on relationship or partner type patterns among avoidant or high-avoidant (i.e., fearful: high avoidance and high anxiety; or dismissive: high avoidance and low anxiety) females is scarce, and there are no studies examining condom use within the context of casual sexual encounters among high-avoidant or avoidant females.

Nonetheless, the literature on partner type and avoidant attachment among females (Gentzler &

Kerns, 2004), considered in tandem with the literature on condom use and avoidant attachment among females (Cooper, Shaver, et al., 1998; J. A. Feeney et al., 2000) suggests that

avoidant/high-avoidance (i.e., dismissive or fearful) females may have more casual partners than steady partners; however dismissive females and fearful females may differ with regard to condom use when having sex with casual partners. Dismissive or avoidant females may have a tendency to use condoms with casual partners (Cooper, Shaver, et al., 1998; J. A. Feeney et al.,

2000). As previously noted, fearful attachment represents a combination of anxious and avoidant characteristics, which may idiosyncratically interact or fluctuate in saliency or expressiveness depending on external and interpersonal factors. That being so, the relationship between fearful attachment and partner type, and whether partner type moderates the relationship between fearful attachment and condom use is unclear.

To summarize, attachment anxiety among females may be associated with a tendency to have unprotected sex with casual partners. Similarly, avoidance is associated with a tendency to have sex with casual partners; however, avoidant females may be more likely than their anxious counterparts to use condoms with casual partners. Condom use with casual partners among women with high levels of both anxiety and avoidance remains to be determined. It may be that fearful women’s wariness and distrust of others (i.e., negative working model-other) leads them to use condoms with casual partners. However, since fearful attachment among women may present more often as anxious than avoidant, it is possible that fearful attachment is associated with a reduced likelihood of using condoms with casual sex partners. An aim of the current study was to clarify the relationship between working model-self and working model-other and condom use with casual partners. As attachment anxiety corresponds with preoccupied attachment and is associated with unprotected casual sex, it is possible that a negative working model-self and a positive working model-other are associated with unprotected casual sex.

Association between attachment and having sex while drinking alcohol. Evidence suggests that having sex while intoxicated reduces the likelihood of using condoms with casual partners, which increases the risk of contracting or transmitting HIV/STI. For instance, Kiene,

Barta, Tennen, and Armeli (2009) found that consuming alcohol before sexual activity decreased the likelihood of using a condom with a casual partner. Particularly among women, there is a positive association between number of drinks consumed and the likelihood of unprotected sex with a casual partner, however there is a negative association with steady partners (Kiene et al.,

2009). Similarly, Brown and Vanable (2007) found that consuming alcohol prior to sexual activity is associated with an increased likelihood of unprotected sex with a non-steady partner.

While these studies speak to the established relationship between having sex while intoxicated and engaging in risky sexual behaviors during sexual encounters that occur while intoxicated, few studies have considered the role of attachment in the tendency to have sex while intoxicated.

Only one study (J.A. Feeney et al., 2000) has examined the association between attachment and the use of drugs/alcohol before sex. J.A. Feeney et al. (2000) found that anxiety over relationships, a key aspect of anxious or high-anxious (i.e., preoccupied, fearful) attachment, is associated with increased likelihood of drug/alcohol use before sex.

Generally speaking (i.e., not necessarily referring to sexual encounters), anxious and avoidant individuals have higher levels of lifetime substance use than secure individuals

(Caspers, Cadoret, Langbehn, Yucuis, & Troutman, 2005). Moreover, McNally, Palfai, Levine, and Moore (2003) found that negative working model-self, which is an aspect of fearful and preoccupied attachment, predicted more drinking-related problems (e.g., driving while intoxicated, missing class); indicating that alcohol use among individuals with a negative working model-self is associated with more negative outcomes/consequences. Although McNally

et al. (2003) did not assess whether insecure individuals or individuals with a negative working model-self are more likely to have unprotected sex with a casual partner or stranger while intoxicated, this may be a potential negative consequence of consuming alcohol, one that increases risk for contracting HIV or STI. The current study will examine the nature of the relationship between negative working model-self and the tendency to have sex while intoxicated, and furthermore to determine whether a positive working model-other is associated with the tendency to have sex while intoxicated, as it is perhaps associated with other risky sexual behaviors. It may be that individuals with a negative working model-self have a tendency to consume alcohol and have sex while intoxicated as a means of coping with their negative views of themselves or low perceived self-worth. Similarly, individuals with a positive working model-other may have a tendency to have sex while intoxicated because alcohol enhances their predisposition to actively engage with others from whom they desire approval.

Summary of the existing literature on the association between attachment and risky sexual behavior. Overall, the existing literature suggests that insecure attachment (anxious, avoidant, preoccupied, fearful, dismissive) is associated with greater engagement in sexual behaviors that increase the risk of contracting or transmitting HIV/STI. Collectively, anxious or high-anxious (i.e., fearful or preoccupied) attachment orientations are associated with less consistent/frequent condom use, more sexual partners, more casual partners relative to steady partners, less frequent condom use with casual partners, and more frequent use of drugs or alcohol before sex. Avoidant or high-avoidant (e.g., dismissive) attachment styles are associated with more consistent/frequent condom use (among men), fewer sexual partners, and more casual partners relative to steady or main partners. Nonetheless, several gaps in the literature exist, most notably the lack of research on the relationship between working model-self and -other and

risky sexual behavior among females. Determining the nature of the relationship between working models and risky sexual behavior may identify constructs that have not yet been considered in the design and implementation of HIV prevention interventions.

Potential Mediators of the Relationship Between Attachment and Risky Sexual Behavior

The current study extends the literature suggesting that attachment style and attachment- related constructs (i.e., working model-self and –other) are associated with risky sexual behavior by investigating potential mediators that may underline this association. The following is a review of constructs that may mediate the relationship between attachment and risky sexual behaviors. This review will focus on mediators that are (1) associated with both attachment and risky sexual behavior and (2) are core elements of effective primary HIV prevention interventions aimed at reducing risky sexual behavior among African American young women.

Specifically, the potential mediating role of partner communication self-efficacy, fear of condom negotiation, peer norms for risky sex, partner trust, and sex-related alcohol outcome expectancies is reviewed.

Partner communication and fear of condom negotiation as mediators of the association between attachment and condom use. Partner communication and condom negation skills are common elements of effective HIV prevention interventions for African

American women (DiClemente et al., 2009; DiClemente et al., 2004; Jemmott, Jemmott,

Braverman, & Fong, 2005; Stanton et al., 1996 as cited by Sales, Lang, et al., 2012). For instance, partner communication skills to negotiate condom use with male partners partially explain increased proportion of condom use (number of times condom used during vaginal sex out of total number of vaginal sex acts) and condom use consistency up to a year after an HIV prevention intervention (Sales, Lang, et al., 2012). Engagement in condom use involves

negotiation skills to introduce the topic and insist on condom use and/or resist partner attempts to dissuade condom use (Boyer, 1990; Fisher & Fisher, 1992 as cited by J.A. Feeney, Kelly,

Gallois, Peterson, & Terry, 1999).

Literature suggests that attachment styles are associated with general communication style and assertiveness. For example, Jang, Smith, & Levine (2002) found that secure individuals are more likely to talk about an issue; anxious to talk around or avoid the issue; and avoidant to avoid a partner and terminate a relationship following relationship conflict. With regard to communication about sexual topics, anxious and avoidant attachment are both associated with inhibited sexual communication (e.g., “I tend to be inhibited about talking about sex”).

MacDonanld, McKenna, and Mouck (2016) reported that perceived rejection from a sexual partner for suggesting using a condom was associated with intentions to have unprotected sex among women high in attachment anxiety and low in attachment avoidance (preoccupied) and women low in attachment anxiety and high in attachment avoidance (dismissive). High levels of attachment anxiety are associated with (a) reduced likelihood of using condoms (Strachman &

Impett, 2009), (b) avoidance of discussing safe sex practices, and (c) the belief that condoms interrupt foreplay, reduce intimacy and destroy spontaneity (Cooper et al., 2006). Furthermore,

Kobak, Herres, Gaskins, and Laurenceau (2012) found that preoccupied females were more likely to engaging in risky sexual behaviors than preoccupied males, and suggest that this may be due to a reduced capacity to negotiate condom use with male sexual partners.

Summary and limitations. To summarize, insecure attachment (defined as anxious, avoidant, preoccupied, fearful, dismissive) is associated with inhibited sexual communication.

Anxious individuals may be less assertive and inhibited due to worry over offending or turning off a partner from whom they are seeking approval or want to please. Avoidant attachment may

result in inhibited sexual communication due to discomfort with intimate self-disclosure and a disinterest in emotional expression. Although there is evidence that attachment style is related to distinct interpersonal communication patterns and inhibited sexual communication, there are relatively few studies that assess the relationship between attachment style and partner communication regarding safe sex, and no studies that specifically assess the relationship between attachment style, condom negotiation skills, and risky sexual behaviors.

Communication inhibition that is linked to attachment anxiety may result in less frequent or consistent condom use, whereas inhibition linked to attachment avoidance may be more likely to affect communication of sexual desires and emotions. It is unclear how partner communication inhibition related to avoidance affects condom negotiation or condom use behaviors. Further, there are no studies that assess whether a negative or positive working model-self or -other is associated with inhibited sexual communication, and if communication self-efficacy partially explains the association between working model-self and -other and condom use. Additionally, there are no studies on the nature of the relationship between fear of condom negotiation and working model-self and –other, and whether fear of condom negotiation partially explains the association between working model-self and –other and condom use.

Peer norms for risky sexual behavior as a mediator of the relationship between attachment and condom use. Social networks (i.e., peer groups) may differ in the degree to which risky sexual behaviors such as having sex while under the influence of drugs or alcohol, having sex with a new acquaintance, and not using condoms are acceptable and endorsed

(Ramiro, Sillero, & Bermúdez, 2013). Literature suggests that perceiving risky sexual behaviors as being normative among one’s peer group is associated with engaging in risky sexual behaviors. As such, HIV prevention intervention experts suggest that fostering more positive

peer norms of safe sex may enhance the overall effectiveness of interventions designed for young

African American females (Crosby et al., 2003). African American females may be particularly sensitive to the effects of perceived peer norms for risky sexual behavior. Among low-income

African American youths/young adults who endorsed peer norms for risky sexual behaviors, females reported less consistent condom use than their male counterparts (Norris & Ford, 1998).

Indeed, peer norms for risky sex are associated with having sex while under the influence of drugs or alcohol and inconsistent condom use among young African American females (Crosby et al., 2003; Voisin, Hotton, Tan, & DiClemente, 2013). Furthermore, one study found that peer norms for risky sexual behavior mediated the relationship between insecure mother-daughter attachment and engagement in sexual HIV-risk behaviors among young African American females (Emerson, Donenberg, & Wilson, 2012). The current study will extend these findings by examining the mediating role of peer norms for risky sexual behaviors in the relationship between the attachment constructs working model-self and –other and condom use among a slightly older sample of African American females.

Partner trust as a mediator of the relationship between attachment style and condom use. Partner trust may be an additional mediator of the relationship between attachment and condom use. Trust in one’s sexual partner has been associated with reduced condom use among women (Brady, Tschann, Ellen, & Flores, 2009; Shahnazi, Forouzan, Nedjat, Asgari, &

Majdzadeh, 2013); women who trust their partners perceive their risk for HIV/STI as being low.

HIV-infected heterosexual individuals report that prior to infection they had high trust in the partner who infected them, low levels of perceived HIV risk, and high levels of perceived partner safety (T. L. Crowell & Emmers-Sommer, 2001). Conversely, low partner trust or

suspecting that one’s main partner may be unfaithful is associated with consistent condom use and fewer instances of unprotected sex (Brady et al., 2009).

Attachment style is a factor that may impact general trust in others, as well as how an individual handles low levels of trust in relationships. A series of cross-sectional studies found that secure attachment is associated with higher levels of trust within the context of romantic relationships than insecure attachment (Brennan & Shaver, 1995; J. A. Feeney & Noller, 1990;

Mikulincer & Erev, 1991). Furthermore, attachment style is associated with appraisal of negative trust-related experiences with a romantic partner (e.g., “My partner promised to return earlier at home, but arrived three hours later”), and the coping strategies that are used to manage those appraisals (Mikulincer, 1998). Anxious persons are more likely to focus on the impact of the negative trust-related experience on the security of the relationship, and react with ruminative worry. Avoidant individuals are more likely to interpret negative trust-related experiences in terms of feelings of lost control in the relationship, and react by distancing themselves from partners. There are no studies that have assessed the relationship between attachment style or attachment –related constructs and sexual strategies for coping with partner distrust, and the effect these strategies may have on condom use (i.e., avoiding intimacy by increasing condom use with a main partner, reducing condom use to communicate trust or confirm relationship security). To address the gap in the literature, the current study will assess if level of partner trust is a partial mediator of the relationship between working model-self and –other and condom use with main sexual partner among women who are in a steady relationship with a male partner.

Alcohol outcome expectancies as a mediator of the relationship between attachment style and having sex while drinking. Alcohol outcome expectancies refer to specific beliefs regarding the effects of alcohol on one’s thoughts and behaviors (Fromme, Stroot, & Kaplan,

1993). Cross-sectional research suggests that sexual enhancement alcohol expectancies and disinhibition alcohol expectancies are associated with more frequent sexual encounters that occur while drinking alcohol (Leigh, 1990). Qualitative data indicates that there are several beliefs commonly held by African American women about the effects of alcohol on sex. These sex- related alcohol expectancies include: drinking will enhance desire to engage in risky sexual behavior; enhance sexual satisfaction; and decrease inhibition. African American women also report the belief that alcohol will promote sexual prowess; reduce sex-related guilt for engaging in regrettable acts; and stimulate openness to anal sex (Hutton et al., 2015). Based on these expectancies regarding the effects of alcohol on sex, African American females may be likely to have sex after consuming alcohol, and these sexual encounters may involve increased sexual risk behaviors (e.g., unprotected vaginal or anal sex, sex with a risky partner). Indeed, higher rates of alcohol use are correlated with having multiple partners, future STI, and never using a condom with a casual partner among African American women (Seth, Wingood, DiClemente, &

Robinson, 2011). However, Seth, Wingood et al. (2011) did not specifically assess whether these risky sexual behaviors were more likely to occur in the context of alcohol use. Nonetheless, in a separate study, Seth, Sales, DiClemente, and Wingood (2011) did find that higher levels of alcohol use predicted having sex while using drugs or alcohol; however the specific sexual-risk behaviors involved in these sexual encounters were not reported.

Only one study has assessed the relationship between attachment style and having sex after consuming alcohol. As previously noted, J.A. Feeney et al. (2000) report that anxiety over relationships, a key aspect of anxious or high-anxious (i.e., preoccupied, fearful) attachment, is associated with drug/alcohol use before sex. There are no studies that have assessed the relationship between attachment style or attachment constructs (i.e., working model-self and –

other) and sex-related alcohol expectancies. However, it has been shown that drinking to cope motives (e.g., “I used alcohol to make myself feel better”) mediate the relationship between anxious attachment and drinking without one’s partner, but do not fully mediate the relationship between anxious attachment and drinking with one’s partner (Levitt & Leonard, 2015). This finding suggests that there may be other constructs, or perhaps other drinking motives, that may explain the relationship between anxious attachment and drinking with one’s partner. Although these findings are only loosely related to the current study, they do suggest that cognitive constructs such as drinking motives and alcohol expectancies may partially explain the relationship between attachment and drinking behaviors. As such, the current study will assess the mediating role of sex-related alcohol expectancies in the association between working model- self and -other and sex while drinking.

Summary of mediators in relationship between attachment and risky sexual behaviors. Various constructs such as partner communication, fear of condom negotiation, peer norms for risky sexual behaviors, partner trust, and sex-related alcohol expectancies may be associated with adult attachment styles and working models, and may partially explain the relationship between attachment and risky sexual behaviors (condom use, sex while drinking).

Being that working models are composed of various cognitions, emotions, expectancies, and previous experiences, it is unlikely that the proposed mediators (which are risk factors for risky sexual behavior that are often addressed by HIV prevention interventions) account for all of the variance in risky sexual behaviors that can be explained by working models. Demonstrating that these psychosocial constructs targeted within HIV prevention interventions (i.e., the proposed mediators) independently explain a proportion of the variance in risky sexual behavior accounted for by working models has clinical implications. Working models represent core schematic

beliefs that are tied to a constellation of variables that affect engagement in risky sexual behaviors (e.g., cognitions, emotions, gender-norms, etc.). If working models increase engagement in risky sexual behaviors through their effect on psychosocial risk factors for risky sexual behaviors, then these findings may help identify females who may not be responsive to

HIV prevention interventions that target these psychosocial risk factors (i.e., mediators). In this regard, low working model-self and high working-model other scores may be used to screen females who may benefit from addressing their schema-like working models before engaging in

HIV prevention interventions that target these mediators (e.g., partner communication, fear of condom negotiation, peer norms for risky sex, sex-related alcohol expectancies). Being that there is a gap in the literature regarding the mediating role of the proposed psychosocial factors in the association between working models and risky sexual behavior, preliminarily examining each mediator separately allows for an understanding of the extent to which an individual construct (e.g., partner trust) mediates the association between working models and sexual risk behaviors. Subsequently examining the associations between working models and the proposed mediators within the context of a comprehensive theoretical framework may further our understanding of the association between attachment, psychosocial risk factors, and sexual risk behaviors among African American young women.

The Theory of Gender and Power as Applied to HIV-risk Disparities between Males and

Females

In addition to examining individual mediators of the relationship between attachment- related constructs (working model-self and –other) and risky sexual behaviors, an aim of the current study is to assess the relative contribution of these constructs to a key theoretical model underlying HIV prevention interventions for young African American women, the Theory of

Gender and Power. Gender- and culturally-tailored interventions for young African American women have been shown to be effective in reducing HIV-risk behaviors and STI incidence, particularly when interventions address empowerment issues and negotiation skills for safer sex

(Crepaz et al., 2009). The Theory of Gender and Power (TGP; Connell, 1987) is a core theory that has been used to design gender-tailored interventions that have been effective in reducing both HIV-risk behaviors and STI incidence among African American young women

(DiClemente et al., 2009; DiClemente et al., 2004). Wingood and DiClemente (2000) discuss

HIV-health related disparities within the Theory of Gender and Power framework, and suggest that power differentials between males and females operate such that women, particularly minority (African American and Latina) women, have an increased vulnerability for acquiring

HIV/STI. According to this model, there are social and behavioral risk factors and exposures that occur within three independent structures and contribute to females’ increased vulnerability for contracting HIV/STI. These three structures, as defined by Connell (1987) and Wingood and

DiClemente (2000) are: Sexual Division of Labor, Sexual Division of Power, and the Structure of Affective Attachments and Social Norms. These structures operate simultaneously, and each structure exists on both a societal and institutional level. The operation of these structures at the societal and institutional levels results in gender inequality, women’s economic and social disadvantage, women having little control of resources, and the establishment of [sexual] behavioral expectations based on gender-norms. It is suggested that these disparities among women result in an increased burden of HIV/STI among African American women. Each of the

TGP structures is associated with acquired risks and risk factors. Acquired risks (i.e., exposures) are factors that increase the probability of contracting HIV. Risk factors (i.e., knowledge, attitudes, beliefs and skills) are psychosocial factors associated with behaviors that increase

HIV/STI-risk. The following is an overview of the exposures and risk factors within each of the

TGP structures that contribute to African American women’s disproportional vulnerability for

HIV/STI (Wingood & DiClemente, 2000).

According to the TGP, the structure of the Sexual Division of Labor refers to the economic inequality women are subjected to through sex-segregated work, unequal pay, and unpaid “nurturing work.” Economic exposures and socioeconomic risk factors that increase women’s vulnerability for acquiring HIV include: living at poverty level, having less than a high school education, being unemployed or underemployed, working in a high demand/low control environment, having limited health insurance or no health insurance, having no permanent home, being under 18, and being an ethnic minority (Wingood & DiClemente, 2000).

Power, within the context of the Structure of the Sexual Division of Power may refer to

“having capacity to influence the action of others” (i.e., power over others), or as “power to act or change in the desired direction” (Wingood & DiClemente, 2000, p. 543). Power differentials can be the product of women depending on males for financial assets or the sexual degradation or disempowerment of women in the media. Physical exposures and behavioral risk factors that increase women’s vulnerability for acquiring HIV include: having a history of being sexually or physically abused, having a partner who disapproves of practicing safe sex, having a high-risk steady male partner (e.g., an IV drug user, a man who patronizes prostitutes, or a man who has multiple sexual partners; Plichta, Weisman, Nathanson, Ensminger, & Robinson, 1992), being exposed to sexually explicit media, having limited access to HIV prevention, having a history of alcohol and drug abuse, having poor assertive communication skills, having poor condom-use skills, having lower perceived self-efficacy to avoid HIV, and having limited perceived control over condom use (Wingood & DiClemente, 2000).

Regarding the Structure of Affective Attachments and Social Norms as it relates to HIV disparities, social norms refer to society’s expectations of female sexuality, and beliefs regarding how women should express their sexuality (Wingood & DiClemente, 2000). These beliefs reinforce strict gender roles and stereotypes, such as the belief that women should only have sex for procreation, and the belief that having multiple partners is acceptable behavior for men, but not for women (Wingood & DiClemente, 2000). Affective attachments refer to the emotional dynamics between women and men that result from social norms regarding sexuality; for example, the tendency for younger women to be sexually attracted to older men, the idea that women’s sexual desire is based on their desire to conceive, and the belief that asking a man to use a condom is disrespectful because it implies that he is unfaithful. The expression of attachment style within sexual/romantic relationships may be similar to affective attachments being that both constructs are related to relational dynamics that may be influenced by gender- norms. Nonetheless, attachment style and affective attachments are conceptually distinct.

Attachment style is not by definition the product of sexual stereotypes or social norms, rather it reflects mental representations of self and other based on previous personal experiences.

Social exposures and personal risk factors within the Structure of Affective Attachments and Social Norms include: having an older partner, having a desire or a partner who desires to conceive, having conservative cultural and gender norms, having a religious affiliation that forbids use of contraception, having a strong mistrust of the medical system, having family influences not supportive of HIV prevention, having limited knowledge of HIV prevention, negative beliefs not supportive of safer sex, having perceived invulnerability to HIV/AIDS, and having a history of depression/psychological distress (Wingood & DiClemente, 2000).

Women exposed to a greater number of economic acquired risks (Sexual Division of

Labor), physical acquired risks (Sexual Division of Power), and social acquired risks (Structure of Affective Attachment and Social Norms) have a higher probability of contracting HIV.

Likewise, women who endorse a greater number of socioeconomic risks factors (Sexual Division of Labor), behavioral risk factors (Sexual Division of Power), and personal risk factors

(Structure of Affective Attachment and Social Norms) are more likely to engage in behaviors that increase risk of contracting HIV/STI (Wingood & DiClemente, 2000). Being that attachment style operates on the interpersonal (social) and intraindividual (personal) levels and may be associated with gender norms and behaviors that increase HIV risk, attachment may be relevant to consider within the TGP framework of HIV-risk behaviors among African American females.

Attachment, gender norms, and power. According to TGP, gender-determined roles and gender-norms/stereotypes that sexually disempower women (and heighten their vulnerability for HIV) are perpetuated by long-standing social conventions that dictate societal expectations about female sexuality. Women who ascribe to conventional gender-based expectations of women’s sexuality (e.g., men hold all the power in a sexual relationship, it is a man’s decision to wear or not wear a condom) are more likely to be at risk for contracting HIV. Conventional expectations regarding women’s sexuality come to shape women’s perceptions of themselves and their sexual partners, and their sexual relationships (Wingood & DiClemente, 2000).

Therefore, according to TGP, gender stereotypes of sexuality shape perceptions of self and other, particularly within sexual relationships; however, perception of self and other are not included in

TGP as distinct constructs related to HIV-risk behaviors. The attachment-related constructs of working model-self and working model-other may be important to consider within the TGP framework, as working model-self and working model-other provide direct indices of perceptions

of self and other. Furthermore, as discussed in an earlier section, working model-self and –other include aspects of gender identity and gender roles that are influenced by cultural gender-norms and stereotypes. The following is a review of the relationship between gender-norms, attachment and risky sexual behavior.

As previously noted, avoidant males are likely to report that they used condoms every time they have sex, but avoidant females are not (J. A. Feeney et al., 2000). Attachment may be related to conventional gender-norms regarding sexuality, with females deferring to the desires of a male partner or perhaps believing that the decision to wear a condom is ultimately a man’s choice. As suggested by attachment researchers and theorists, gender differences in the sexual behaviors associated with particular attachment styles may be reflective of gender differences in the content of mental representations of self and other, or in the relative salience of working model-self and –other in determining behavior. These gender differences in working model-self and working model-other are thought to be the product of gender-based norms that become integrated aspects of these attachment constructs. Particularly, with regard to sexual risk behaviors, gender-norms of sexual behavior that disempower women may be integrated into working models, and as such, affect engagement in risky sexual behaviors.

Ascribing to traditional gender roles, which tend to disempower women, is associated with increased barriers to condom use among women (Shearer, Hosterman, Gillen, & Lefkowitz,

2005), and women are more likely to use condoms when they perceive themselves as being more powerful in the relationship (Harvey, Bird, De Rosa, Montgomery, & Rohrbach, 2003). Extant literature suggests that insecure attachment style is associated with lower levels of perceived relationship power (Radecki-Bush & Bush, 1991), and gender-based motivations for engaging in risky sexual behavior (Lemelin, Lussier, Sabourin, Brassard, & Naud, 2014). Females tend to

engage in risky sexual behavior to avoid abandonment and rejection, whereas males are motivated to engage in risky sexual behavior for opportunistic reasons and to impress their peers

(Lemelin et al., 2014). It may be that these patterns are related to power differentials between men and women and sexuality gender-norms. While the TGP model of sexual HIV-risk behaviors among women is based on the effects of gender-roles and stereotypes that disempower women, the addition of working model-self and –other may enhance the overall TGP model because they may represent the internalization of these gender-based norms and stereotypes, and the integration of them in a female’s core conception of herself and others (particularly male sexual partners).

The proposed TGP model with attachment. DePadilla, Windle, Wingood, Cooper &

DiClemente (2011) tested TGP as a theoretical model for predicting HIV-risk behavior engagement among African American young women (see Figure 2 for DePadilla et al.’s model).

The authors found several associations between HIV-risk factors identified by Wingood and

DiClemente (2000) and condom use. Variance in condom use was explained by partner communication skill, substance use during sex, and negative personal affect (which may be related to a negative working model-self). The strongest predictor of condom use among this sample was partner communication skill, followed by negative personal affect. Low working model-self (less favorable view of self) and high working model-other (more favorable view of others) may be potential HIV-risk factors in and of themselves, provided they are associated with a cluster of HIV-risk factors as identified by TGP (e.g., partner communication, substance use during sex, fear of condom negotiation). The current study tested a model similar to the model developed by DePadilla et al. that is based on TGP; however, the current study’s model included working model-self and –other as constructs in the model and assessed the fit of this expanded

model of sexual risk behavior among African American women (see Figure 2 for DePadilla et al.’s model and Figure 3 for the current study’s model).

There were several differences between the model proposed in this study and DePadilla et al.’s (2011) model that should be noted. Frequency of parental communication is not included in the model, as the current study focuses on sexual risk taking among adult African American women, whereas DePadilla et al.’s (2011) sample ranged in age from 14 to 20 years old. Self- esteem is not included in the proposed model because it was not identified by Wingood and

DiClemente (2000) as an affective risk factor. Conservative religious beliefs are not included as an aspect of the latent variable Affective Attachments and Social Norms because they were not assessed in the current study. The control subscale of the Sexual Relationship Power Scale

(Pulerwitz & Gortmaker, 2000) is included in the proposed model (but not in DePadilla et al.’s model) because it provides an index of perceived power within relationships, and may identify power differentials in relationships that do not include more extreme cases such as abuse.

The outcome latent construct in the proposed model (Sexual HIV/STI-Risk Behaviors) includes sexual risks (e.g., number of partners) that were not included in DePadilla et al.’s model. This expands the model to include other sexual HIV-risk behaviors and determine if TGP can explain these additional HIV-risk behaviors that may also contribute to HIV-related disparities among African American women. While condom use behaviors are indicative of an individual’s risk for contracting HIV within a given sex act, other behaviors such as number of sexual partners are also related to risk of HIV-exposure, which is important to consider in understanding HIV-vulnerability disparities. Additionally, variations of the condom use variables used by DePadilla et al. are included in the current model (e.g., proportion condom use in past 3

months, condom use at last sexual encounter involving alcohol) to provide a more comprehensive assessment of condom use behaviors.

Attachment in the proposed model. It is proposed that the Attachment latent variable would be composed of the variables working model-self and working model-other to correspond with Bartholomew and Horowitz’s (1991) model of attachment styles. Attachment is proposed as a separate latent variable, as research indicates that it is associated with sexual risk behaviors

(Sexual HIV/STI-Risk Behaviors), and it is hypothesized to be associated with several other TGP latent variables in the model (e.g., Sexual Division of Power, Structure of Affective Attachment and Social Norms).

The following is a review of the proposed rationale for considering Attachment as a Risk

Factor. Risk Factors are psychosocial constructs that are associated with engaging in risky sexual behaviors (Wingood & DiClemente, 2000). These psychosocial risk factors can be socioeconomic, behavioral, or personal, and they operate at the interpersonal and individual levels (Wingood & DiClemente, 2000). Working model-self and –other are psychosocial constructs; they are mental representations of oneself and others within social relationships and interactions. Furthermore, these constructs operate at the individual and interpersonal levels in several senses; (1) they are internalized constructs (individual) that affect how one experiences and behaves within social relationships (interpersonal), and (2) they include representations of self (individual) and others (interpersonal). Since the current study seeks to determine whether these interpersonal psychosocial constructs, working model-self and -other are associated with engagement in risky sexual behaviors, Attachment is considered a Risk Factor in the TGP model.

Acquired risks are defined as exposures that increase the probability that a disease (e.g., HIV) will later develop (Wingood & DiClemente, 2000). Exposures may be physical, economic, or

social (Wingood & DiClemente, 2000). The addition of Attachment may enhance the TGP conceptualization of HIV-risk because it presents a Risk Factor that may affect exposures (i.e.,

Acquired Risks); specifically, the TGP constructs Sexual Division of Power and Affective

Attachments and Social Norms (see Figure 3 and Figure 4). Although, theoretically, Acquired

Risks and Risk Factors interact with each other (Wingood & DiClemente, 2000), it is unclear whether Acquired Risks and Risk Factors across TGP Structures interact. Moreover, DePadilla et al.’s model of TGP (2011), indicates that Acquired Risks affect Risk Factors (see Figure 2), rather than Risk Factors affecting Acquired Risks, or interacting with each other. The current study seeks to clarify the nature of the relationship between Acquired Risks and Risk Factors by identifying a Risk Factor (i.e., Attachment) that may affect Acquired Risks (exposures) across

TGP Structures. The relevance of a Risk Factor that affects Acquired Risks- a gap that

Attachment may fill- is that it may provide a means for identifying women who are at a higher risk of being exposed to these sociocultural factors, if they are not already. For example, women with a particular attachment style might be more likely to be involved in a relationship with an abusive or domineering man (Acquired Risk). This may, in turn, increase their likelihood of having unprotected sex with said male partner (Risk Factor) through various mechanisms, which thus increases their risk for acquiring HIV or STI. Clarifying the nature of these relationships might assist in identifying African American women who are at risk for entering abusive relationships (or being exposed to other sociocultural risk factors), and experiencing the associated sexual risks.

Furthermore, interventions designed to address core, schema-like constructs such as working models may present a means for decreasing women’s likelihood of future sociocultural exposures (e.g., entering relationships with controlling male partners). Adding Attachment to the

TGP model and clarifying the nature of the relationships between Attachment, TGP constructs and sexual risk behaviors may help identify women who might be resistant to HIV prevention interventions, and require more involved interventions that target these working models before becoming involved in an HIV prevention intervention.

Summary of the potential role of attachment within the Theory of Gender and

Power. The Theory of Gender and Power has been used to explain HIV/STI disparities in terms of the broader, sociocultural factors that impact women’s sexual health and increase women’s vulnerability for acquiring HIV. These sociocultural factors maintain long-held power differentials that are imbued with unfavorable gender stereotypes toward women. Despite being associated with sexual risk behavior (e.g., unprotected sex, number of sexual partners, having casual partners), and various HIV-exposures and risk factors identified by TGP (e.g., partner communication, substance use, peer norms for risky sexual behavior), attachment has not been considered within the TGP model of HIV-risk behaviors. Moreover, TGP suggests that power differentials based on gender stereotypes impact women’s perceptions of themselves and others, and their experiences in sexual relationships (Wingood & DiClemente, 2000); however, TGP does not include specific indices of mental representations of self and other that may also reflect internalized gender-norms (i.e., working-model self and –other). As such, attachment-related constructs may be relevant to consider within the TGP framework of HIV-risk among African

American women, and they may enhance clinical efforts to reduce risky sexual behaviors among high-risk populations. If attachment contributes to the overall ability of TGP to predict sexual risk behaviors, working models may be identified as constructs that affect (a) exposures that increase probability of contracting HIV and (b) risk factors for engaging in risky sexual behaviors. While some women may be responsive to HIV prevention interventions that address

psychosocial risk factors by providing information regarding safer sex skills and reinforcing those skills through intervention strategies, other women may not be. It is possible that working models may represent a mechanism for initiating behavioral changes among nonresponsive women. To the extent that risky sexual behaviors as well as risk factors are associated with certain core attachment-related beliefs, some women may require interventions that target these constructs prior to addressing the risk factors. Changing an individual’s core concept of herself and others is beyond the scope of brief, often group-based HIV prevention interventions.

However, internal working models of self and other may potentially be addressed by schema- focused interventions such as interpersonal therapy, cognitive behavioral analysis system of psychotherapy (CBASP), or cognitive behavioral therapy. Indeed, attachment-based treatments

(ABTs) that target internal working models have recently been developed for adolescents and young adults, particularly treatment resistant individuals (Bevington et al., 2015; Kobak et al.,

2015). However, these treatments are relatively new, and more research is required to establish the efficacy of ABTs.

Emerging Research Priorities

Research suggests that adult attachment affects how individuals behave within and experience romantic and sexual relationships. Studies specifically assessing the relationship between attachment and risky sexual behaviors suggest that insecure attachment (anxious, avoidant, fearful, preoccupied, dismissive) is associated with various forms of sexual risk behaviors such as unprotected sex, increased number of sexual partners, and sex with casual partners or strangers. Studies vary in how attachment is operationalized; however, few studies have assessed the relationship between the attachment-related constructs of working model-self and –other and sexual risk behaviors. Being that lower working model-self scores indicate less

favorable views of oneself, it may be that women with lower working model-self scores have a greater tendency to engage in risky sexual behaviors; perhaps as a means of bolstering one’s view of oneself as attractive, desirable, and lovable. Higher working model-other scores indicate more favorable views of others, and a tendency to define one’s self-worth in terms of how others perceive oneself; thus, women with higher working model-other scores may have a tendency to engage in risky sexual behaviors as a means of gaining acceptance from others (particularly male sexual partners) and validating one’s self-worth. The current study will clarify the nature of the relationship between working model scores and risky sexual behaviors, and furthermore, to determine whether one working model accounts for more variance in particular risky sexual behaviors above and beyond that which is accounted for by the other working model.

In addition, a review of the literature suggests that working model-other may have a stronger effect on initial attraction to an individual, whereas working model-self may be more influential with regards to attraction during relationship maintenance (Holmes & Johnson, 2009).

This suggests that the cognitions and emotions that are associated with a particular working model may be more or less salient depending on the stage of a relationship. Therefore, an additional aim of the present study is to determine whether partner type moderates the relationship between each of the working models and condom use at recent sexual encounters. It may be that working model-self scores have a stronger effect on condom use with a steady partner than with a casual partner, whereas working model-other scores have a stronger effect on condom use with a casual partner than with a steady partner.

Moreover, there is a need to investigate the constructs or behaviors that may partially explain the association between attachment, particularly within the working model framework, and engagement in risky sexual behavior. Research indicates that HIV prevention intervention

targets (i.e., psychosocial risk factors for risky sexual behavior) such as partner communication self-efficacy, fear of condom negotiation, peer norms for risky sexual behavior, partner trust, and sex-related alcohol expectancies may be related to working model-self and working model-other.

The current study will extend the extant literature by 1) clarifying the nature of the relationship between the aforementioned HIV prevention intervention targets and working model-self and – other and 2) determining whether these HIV prevention intervention targets independently, partially mediate the relationship between working model-self and –other and risky sexual behaviors.

Lower working model-self scores and higher working model-other scores may be associated with lower partner communication self-efficacy, perhaps resulting from a reluctance to open oneself up for possible rejection by someone from whom one seeks approval. Reduced confidence in one’s ability to discuss condom use with a male partner may partially explain the associations between lower working model-self scores and higher working model-other scores and a reduced likelihood of using condoms.

Similarly, lower working model-self scores and higher working model-other scores may be associated with higher levels of fear of condom negotiation, again, perhaps resulting from a fear of possible rejection by someone from whom one seeks approval. Fear of condom negotiation stemming from a fear of rejection by a partner may partially explain the relationship between lower working model-self scores and higher working model-other scores and a reduced likelihood of using condoms.

Lower working model-self scores may be associated with greater support of peer norms for risky sexual behaviors, perhaps reflecting a tendency to focus on others’ behaviors as a way to gain approval or “fit in.” Thus, higher working model-other scores may be associated with

greater perceptions of peer norms for risky sexual behavior, reflecting a tendency to align with others’ behavior because one holds others in high regard. The associations between working model-self and -other and perceptions of peer norms for risky sexual behaviors may partially explain the relationship between lower working model-self and higher working model –other scores and a reduced likelihood of using condoms.

Being that there is not sufficient literature regarding the relationship between working model-self and partner trust, the nature of the relationship between working model-self and partner trust remains to be determined; the current study will attempt to clarify the nature of this association. Higher working model-other scores may be associated with higher levels of partner trust, due to a tendency to believe others are trustworthy or loyal. The relationship between working models and higher levels of partner trust may partially explain the relationship between working models and reduced condom use. Women who have a tendency to be more trusting of their male sexual partners, as previously outlined, may be less likely to use condoms with their partners because they believe (or want to believe) that the relationship is monogamous, and therefore there is a low amount of risk associated with having unprotected sex with a trusted male partner.

Lower working model-self scores may be associated with greater sex-related alcohol expectancies, perhaps reflecting a belief that one requires alcohol to feel self-confident or less sexually inhibited. Higher working model-other scores may be associated with greater sex- related alcohol expectancies, perhaps from a desire for others to see oneself as more confident or sexually expressive, because one seeks approval from others. Therefore, the associations between lower working model-self and higher working model-other and greater sex-related

alcohol expectancies may partially explain the relationship between lower working model-self and higher working model-other and the tendency to have sex while intoxicated.

It is hypothesized that the relationship between lower working model-self and higher working model –other scores and risky sexual behavior may be partially explained by constructs and behaviors that are targeted by HIV prevention interventions (e.g., partner trust). Thus, the addition of attachment as operationalized by the constructs working model-self and -other to the

Theory of Gender and Power may enhance this overall model of risky sexual behavior and increased vulnerability for HIV/STI among women. Such information may enrich sexual-health researchers’ understanding of the core psychological constructs that are associated with engagement in sexual HIV-risk behaviors within a comprehensive theoretical framework, and may inform future efforts to improve sexual health and HIV/STI disparities among African

American females.

In summary, the present study will examine the associations between working models and sexual risk behaviors, potential partial mediators of the relationship between working models and sexual risk behavior, and the suitability of the Theory of Gender and Power model of HIV-risk behavior with the added attachment construct in explaining risky sexual behaviors among

African American women.

Aims & Hypotheses

The aims and corresponding hypotheses for this study are:

Aim 1a. Using a multiple regression approach, to examine the associations between working model-self and working model-other (predictors) and sexual HIV-risk behaviors including: (a) proportion of condom use for vaginal and anal sex (past 3 months), (b) number of

sexual partners (lifetime and past 3 months), (c) having a casual sexual partner (past 3 months), and (d) alcohol use before having sex (past 3 months).

Hypothesis 1.1. Lower working model-self scores (indicative of less favorable self- regard; view that one is unlovable and unworthy of respect) will be associated with: (a) lower proportion of condom use for vaginal and anal sex (past 3 months), (b) higher number of sexual partners (lifetime and past 3 months), (c) higher likelihood of having a casual sexual partner

(past 3 months), and (d) higher likelihood of alcohol use before having sex (past 3 months).

Higher working model-other scores (indicative of more favorable other-regard; looking to others for acceptance; view that others are trustworthy) will be associated with: (a) lower proportion of condom use for vaginal and anal sex (past 3 months), (b) higher number of sexual partners (lifetime and past 3 months), (c) higher likelihood of having a casual sexual partner

(past 3 months), and (d) higher likelihood of using alcohol before having sex (past 3 months).

Aim 1b. To explore whether the presence of working model-self in the multiple regression models (Aim 1a) impacts the effect of working model-other on the sexual outcome variables of interest; and similarly, whether the presence of working model-other in the multiple regression models impacts the effect of working model-self on the sexual outcome variables of interest. Furthermore, to determine whether one of the working models (either working model- self or working model-other) is a stronger predictor of the sexual risk behaviors (a-d) described above. Given the exploratory nature, no specific hypotheses are provided.

Aim 1c. Using a multiple regression approach, (1) to determine the relationship between working models (predictors) and condom use at most recent sexual encounter (a) not involving alcohol and (b) involving alcohol; and (2) to determine whether partner type moderates the relationship between the working models and condom use.

Hypothesis 1.2a. Higher working model-other scores and lower working model-self scores will be associated with a lower likelihood of using a condom at the most recent sexual encounter not involving alcohol.

Hypothesis 1.2b. Partner type will moderate the relationship between working model-self scores and condom use at the most recent sexual encounter not involving alcohol such that working model-self scores will have a stronger positive effect on condom use with a steady partner at the most recent sexual encounter not involving alcohol than with a casual partner at the most recent sexual encounter not involving alcohol.

Hypothesis 1.2c. Partner type will moderate the relationship between working model- other scores and condom use at the most recent sexual encounter not involving alcohol such that working model-other scores will have a stronger negative effect on condom use with a casual partner at the most recent sexual encounter not involving alcohol than with a steady partner at the most recent sexual encounter not involving alcohol.

Hypothesis 1.3a. Higher working model-other scores and lower working model-self scores will be associated with a lower likelihood of using at condom at the most recent sexual encounter involving alcohol.

Hypothesis 1.3b. Partner type will moderate the relationship between working model-self scores and condom use at the most recent sexual encounter involving alcohol such that working model-self scores will have a stronger positive effect on condom use with a steady partner at the most recent sexual encounter involving alcohol than with a casual partner at the most recent sexual encounter involving alcohol.

Hypothesis 1.3c. Partner type will moderate the relationship between working model- other scores and condom use at the most recent sexual encounter involving alcohol such that

working model-other scores will have a stronger negative effect on condom use with a casual partner at the most recent sexual encounter involving alcohol than with a steady partner at the most recent sexual encounter involving alcohol.

Aim 2. To examine individual potential partial mediators (partner communication self- efficacy, fear of condom negotiation, peer norms for risky sex, partner trust, and alcohol outcome expectancies) of the relation between working model-self and working model-other and risky sexual behaviors using a multiple regression approach.

Hypothesis 2.1. Partner communication self-efficacy will partially mediate the relationship between working model-self and -other scores and proportion of condom use for vaginal and anal sex (past 3 months). Lower working model-self scores will be positively associated with lower communication self-efficacy; higher working model-other scores will be negatively associated with lower communication self-efficacy. Lower partner communication self-efficacy will be positively associated with a lower proportion of condom use for vaginal and anal sex.

Hypothesis 2.2. Fear of condom negotiation will partially mediate the relationship between working model-self and -other scores and proportion of condom use for vaginal and anal sex (past 3 months). Lower working model-self scores will be negatively associated with higher levels of fear of condom negotiation; higher working model-other scores will be positively associated with higher levels of fear of condom negotiation. Higher levels of fear of condom negotiation will be negatively associated with a lower proportion of condom use for vaginal and anal sex.

Hypothesis 2.3. Peer norms for risky sexual behaviors will partially mediate the relationship between working model-self and -other scores and proportion of condom use for

vaginal and anal sex (past 3 months). Lower working model-self scores will be negatively associated with greater perceptions of peer norms for risky sexual behaviors; higher working model-other scores will be positively associated with greater perceptions of peer norms for risky sexual behavior. Greater perceptions of peer norms for risky sexual behavior will be negatively associated with a lower proportion of condom use for vaginal and anal sex.

Hypothesis 2.4. Partner trust levels will partially mediate the relationship between working model-self and -other scores and condom use with a boyfriend or main partner at last sexual encounter with a boyfriend/main partner. The nature of the relationship between working model-self and partner trust remains to be determined, therefore no specific hypothesis is provided regarding the relationship between level of partner trust and working model-self. Higher working model-other scores will be positively associated with higher levels of partner trust.

Higher levels of partner trust will be negatively associated with a lower proportion of condom use for vaginal and anal sex among women in a steady relationship.

Hypothesis 2.5. Sex-related alcohol expectancies will partially mediate the relationship between working model-self and –other scores and frequency of having sex while intoxicated.

Lower working model-self scores will be negatively associated with greater sex-related alcohol expectancies. Higher working model-other scores will be positively associated with greater sex- related alcohol expectancies. Sex-related alcohol expectancies will be positively associated with frequency of having sex while intoxicated, such that greater endorsement of sex-related alcohol expectancies will be associated with a higher frequency of having sex while intoxicated.

Aim 3. To determine whether a structural equation model based on the Theory of Gender and Power (TGP) with the added construct of attachment (conceptualized according to the working model framework) provides a good fit for sexual HIV-risk behaviors among women;

defined as a latent variable comprised of: (a) proportion of condom use for vaginal and anal sex

(past 3 months), (b) number of sexual partners (lifetime and past 3 months), (c) alcohol use before having sex (past 3 months), (d) condom use at most recent sexual encounter while drinking, and (e) condom use at most recent sexual encounter not involving alcohol.

Hypothesis 3.1. The TGP conceptualization of HIV-risk among African American women with the added construct of attachment will demonstrate good fit as a model of sexual

HIV-risk behavior among African American females. As such, this model will identify the constructs working model-self and –other as variables that affect TGP constructs within the model predicting sexual risk behaviors.

Figures 3 and 4 display the hypothesized paths between Attachment and the TGP latent constructs. These hypothesized paths in the structural model build upon Aims 1 and 2. For example, Hypothesis 2.2 states that fear of condom negotiation will partially mediate the relationship between the working models and proportion of condom use in the past three months.

As such, in the proposed model, it is hypothesized that there will be a path from Attachment

(indicated by working model-self and –other) to Sexual Division of Power (which included fear of condom negotiation as an indicator variable); and a path from Sexual Division of Power to

Sexual HIV/STI-Risk Behaviors (e.g., condom use). Being that Hypotheses 2.1-2.5 hypothesize partial mediation, it is also hypothesized that Attachment will have a direct effect on Sexual

HIV/STI-Risk Behaviors. Overall, the rationale for the proposed paths between Attachment and the TGP latent constructs is an extension of the Aim 2 mediation hypotheses. Each TGP latent construct contains an indicator variable that is hypothesized to partially mediate the relationship between working models and risky sexual behaviors in Aim 2.

Method

Participants

A total of 560 participants completed the baseline assessment and were randomized to study conditions. The current study includes only the baseline data obtain from these 560 African

American young adult females (M age= 20.58, SD = 1.89). Descriptive information regarding the demographic characteristics of the sample is presented in Table 1. The number of forms of government assistance received by the women in this study ranged from 0-4 (M = 1.35, SD =

0.93); and a little over a quarter of the women (27.14%) were currently employed at the time of baseline data collection. The majority of the women (66.07%) had at least a high school diploma or GED, and nearly half (48.39%) lived with their parents. The vast majority of the sample

(84.46%) had a boyfriend or main partner at the time baseline data was collected.

Procedures

African American females (ages 18-24) were recruited from January 2012 until February

2014 via community outreach in Atlanta, Georgia. Young women who self-identified as African

American, were 18-24 years old, not married or pregnant, had consumed alcohol on at least three occasions in the past 90 days, and had had unprotected vaginal sex with a male partner in the past 90 days were eligible to participate in the study. Participants were screened for eligibility prior to the baseline assessment. Eligible participants scheduled baseline assessment appointments and intervention sessions at Emory University. Enrolled participants could refer up to three other young women for eligibility screening, and receive a $5 compensation for each referral successfully enrolled.

Participants provided written informed consent prior to the baseline assessment.

Participants were compensated for each intervention and assessment session completed

throughout the duration of the 12- month study (up to $445). The current study used only the baseline data collected prior to randomization to the clinical trial arm. The Emory University

Institutional Review Board approved all study protocols.

Baseline measures were obtained via an audio computer-assisted self-interview (ACASI) survey that assessed sociodemographics, sexual history, alcohol use, communication skills, and psychosocial constructs associated with STI/HIV-preventive behaviors. All responses were de- identified to protect confidentiality.

Measures

Sociodemograhics. Participants reported their age at assessment. Employment status was assessed by the item, “Do you have a job for which you are paid?” Participants indicated their highest level of education (8th grade or less; Some high school; Graduated high school or

GED; Some college; Graduated college; Other). Participants reported which, if any, forms of government aid they or anyone they live with received in the past year (e.g., welfare, food stamps, WIC, housing subsidies). A total summed score to indicate number of different types of government assistance received was computed; this variable was used in all analyses as an index of government assistance received. Additionally, participants reported with whom they currently live (Alone; With roommate; With boyfriend; With parents; and/or With other people).

Participants indicated their relationship status by responding to the item, “Do you have a boyfriend or main partner” (Yes; No).

Adult attachment. The Relationship Questionnaire (RQ Bartholomew & Horowitz,

1991) was used as an index of adult attachment. Each item on the RQ describes a different attachment style (secure, preoccupied, fearful, dismissive). For example, the description of preoccupied that was presented to participants was, “I want to be really close to people but they

don't want to be close to me. I am unhappy if I don't have people that I am close to. I sometimes think that I care more about people than they care about me.” Participants were asked to rate how characteristic each attachment style description is of them on a four-point scale (1-Not at all like me; 4- Very much like me).

Working model-self and –other. Working model-self (WMS) and –other (WMO) were computed for each participant based on that participant’s ratings of all four RQ items; Working model-self =[(secure+dismissive)-(preoccupied+fearful)] and working model-other

=[(secure+preoccupied)-(dismissive+fearful)]. Thus, higher WMS scores indicated more positive self-regard (e.g., lovability, internally established sense of self-worth), and higher WMO scores indicated more positive other-regard (e.g., trustworthy, accepting; Griffin & Bartholomew,

1994). The RQ is a validated measure of adult attachment, and the dimensions of WMS and

WMO have demonstrated construct validity (Griffin & Bartholomew, 1994).

Dominant attachment style. For descriptive purposes, dominant attachment style was determined by the item with the highest rating on the RQ, using a methodology described by

Ciesla et al. (2004); fearful took precedence over other styles in the event of a tie and dismissing and preoccupied took precedence over secure in the event of a tie (Ciesla et al., 2004). When using the dominant attachment style method to classify participants, some information may be lost, such as the variability between the ratings on the four items; perhaps fearful was the

“dominant” attachment style, but it was rated only one point higher than the preoccupied description (higher ratings indicated that the description was more characteristic of the individual).

Risky sexual behaviors. Items that assessed risky sexual behaviors are standardized measures employed in prior HIV prevention intervention randomized controlled trials

(DiClemente et al., 2014).

Proportion condom use. Participants reported the number of times they had vaginal and anal sex in the past 3 months. Items assessing condom use included: “Out of the ___ times you’ve had [vaginal] [anal] sex in the past 3 months, how many times did you use a condom?”

Proportion condom use for vaginal sex and for anal sex were calculated separately by dividing the number of times a condom was used during vaginal/anal sex by the number of times an individual had vaginal/anal sex in the past three months.

Number of sexual partners. Participants indicated how many sexual partners they had vaginal sex with in their lifetime, and in the past 3 months.

Casual partners in past three months. Participants responded to the following item, “In the past 3 months, did you have a casual sex partner?” (Yes; No).

Alcohol use before sexual encounters. Participants reported how frequently they had sex while under the influence of alcohol in the past three months by responding to the item, “In the past 3 months, how much of the time did you drink alcohol before you had sexual intercourse?” (responses range from 1-Never to 5-Always).

Partner type and condom use at last sexual encounter not involving alcohol. To address Hypothesis 1.3 (Aim 1c), participants responded to the following items: “Which best describes the partner you most recently had sex with when you were NOT under the influence of alcohol?” (Someone I just met; A casual sex partner; My boyfriend) and “The last time you had sex when you had NOT been drinking alcohol, did you use a condom?” (Yes; No).

Partner type and condom use at last sexual encounter involving alcohol. To address

Hypothesis 1.4 (Aim 1c), participants responded to the following items: “Which best describes the partner you most recently had sex with while under the influence of alcohol?” (Someone I just met; A casual sex partner; My boyfriend) and “The last time you had sex when you had been drinking, did you use a condom?” (Yes; No; I do not remember). Participants also indicated how drunk they were at most recent sexual encounter involving alcohol (Not very; A little;

Moderately; Extremely).

Condom use with boyfriend or main partner. To address Hypothesis 2.4, participants who reported that they had a boyfriend or main partner at time of baseline assessment responded to the following item: “The last time you had sex with your boyfriend or main partner, did he use a condom?” (Yes; No).

Mediator variables. The following measures were used to assess the mediators variables: (a) partner communication self-efficacy, (b) fear of condom negotiation, (c) peer norms for risky sex, (d) partner trust, and (e) alcohol expectancies.

Partner communication self-efficacy. The partner communication self-efficacy subscale of the Partner Communication History measure was used to assess partner communication self- efficacy (Wingood & DiClemente, 1998b). This subscale consists of six items. An example item is, “How hard is it for you to ask how many sex partners he has had?” Participants responded to each item on a four-point scale (1-Very Hard; 4-Very Easy). Responses were summed for a total score (ranging from 6-24), with higher scores indicating higher levels of partner communication self-efficacy. Cronbach’s = .86, indicating good internal consistency (D. George & Mallery,

2003).

Fear of condom negotiation. Seven items assessed level of fear of condom negotiation

(DiClemente et al., 2001). An example item is, “I have been worried that if I talked about using condoms with my boyfriend or sex partner he would threaten to leave me.” Participants responded to each item on a five-point scale (1-Never; 5-Always). Responses were summed for a total score (ranging from 7-35), with higher scores indicating greater fear of communicating about condoms with a male sexual partner. Cronbach’s = .90, indicating excellent internal consistency (D. George & Mallery, 2003).

Peer norms for risky sexual behavior. Peer norms for risky sexual behaviors were assessed by a five-item scale that has been standardized and used in previous HIV prevention intervention trials (Stanton et al., 1995). Items included, “How many of your friends think that it’s okay to have vaginal or anal sex without a condom?” (1-None; 2-Few; 3- Some; 4- Most; 5-

All). Higher scores indicated higher perceptions of risky sexual norms among peers. Cronbach’s

=.78, indicating acceptable internal consistency (D. George & Mallery, 2003).

Partner trust. The Partner Trust Scale (Rempel, Holmes, & Zanna, 1985) is a five-item scale that assessed level of partner trust among women who reported that they were in steady relationship with a main partner at the time of baseline assessment. A sample item includes,

“Based on past experiences, I cannot rely on my partner to keep promises made to me.”

Participants responded to each item on a seven-point scale (1-Strongly disagree; 7-Strongly agree). Responses were summed to produce a total score (ranging from 5-35), with higher scores indicating greater trust in one’s partner. Cronbach’s = .56, indicating poor internal consistency

(D. George & Mallery, 2003). Reliability analyses indicated that removing the item, “I can’t always be sure what my partner will surprise me with” would increase Cronbach’s  to .71; as such, the current analyses do not include this item in the total score for partner trust.

Alcohol expectancies. Sex-related alcohol outcome expectancies were assessed using the four sexuality items from The Comprehensive Effects of Alcohol (CEOA) questionnaire

(Fromme et al., 1993). An example item is, “If I were under the influence from drinking alcohol

I would enjoy sex more.” Participants responded to each item on a four-point scale (1- Disagree;

4-Agree). A total score was computed as the sum of scores on each item (range 4-16), with higher score indicating greater agreement that alcohol produces sex-related effects. Cronbach’s

= .90, indicating excellent internal consistency (D. George & Mallery, 2003).

Variables to assess model fit of TGP and enhanced-TGP models of HIV-risk behavior. The following measures were used to assess indicator variables that were included in the current study’s TGP model of HIV-risk behavior. As depicted in Figure 3, the following measures were proposed as indicators of the latent variables: Attachment, Sexual HIV/STI-risk behaviors, Sexual division of labor, Sexual division of power, Affective attachments and social norms, Affective personal risk, Knowledge based personal risk, and Behavioral risk.

Attachment. Working model-self and working model-other were measured as described above.

Sexual HIV/STI risk behaviors. The proposed indicators for the Sexual HIV/STI risk behavior latent variable were measured as described above: (a) proportion of condom use for vaginal and anal sex (past 3 months), (b) number of sexual partners (lifetime and past 3 months),

(c) alcohol use before having sex (past 3 months), (d) condom use at most recent sexual encounter while drinking, and (e) condom use at most recent sexual encounter not involving alcohol.

Sexual division of labor. Variables described above were used to assess acquired risks within the sexual division of labor. These variables included: education; employment status; and government assistance.

Sexual division of power. The Sexual division of power latent variable represented the acquired risks (i.e., physical exposures) associated with the Structure of Sexual Division of

Power. The control subscale of the Sexual Relationship Power Scale (Pulerwitz & Gortmaker,

2000) measured control within relationships. The control subscale consists of nine items. An example item is, “My partner does what he wants even if I don't want him to.” Participants responded to each item on a four-point scale (1-Strongly disagree; 4-Strongly agree). The items were reverse coded and a total score was computed by summing the scores of each item; higher scores indicated higher levels of perceived relationship control. Cronbach’s =.87, indicating good internal consistency (D. George & Mallery, 2003). Coerced sex was measured by the item,

“Has anyone ever forced you to have vaginal sex when you didn’t want to?” (Yes; No). Physical abuse was assessed by the question, “Have you ever been physically abused? (hit, punched, kicked, slapped, etc.)” (Yes; No). Emotional abuse was measured by the item, “Have you ever been emotionally abused? (threatened, called names, etc.)” (Yes; No). Fear of condom negotiation was measured using the seven-item scale described above.

Behavioral risk. The Behavioral risk latent variable represented the risk factors (i.e., behavioral risks) associated with the Structure of Sexual Division of Power. Alcohol use before sex was assessed using the item described above: “In the past 3 months. How much of the time did you drink alcohol before you had sexual intercourse” (responses range from 1-Never to 5-

Always). Sexual refusal self-efficacy was measured using the seven-item Sexual Refusal Self-

Efficacy Scale (Ebreo, Feist-Price, Siewe, & Zimmerman, 2002). An example of an item on this

measure is, “How sure are you that you would be able to say NO to having sex with someone who refuses to wear a condom?” (1- I definitely can’t say no; 4- I definitely can say no). Higher scores on this measure indicated greater sexual refusal self-efficacy. Cronbach’s = .82, indicating good internal consistency (D. George & Mallery, 2003). Frequency of partner communication was measured using the five-item partner communication frequency subscale of the Partner Communication History Scale (Wingood & DiClemente, 1998b). Higher scores on this subscale indicated more frequent partner communication about sex. Cronbach’s = .86, indicating good internal consistency (D. George & Mallery, 2003). Partner communication self- efficacy was measured using the partner communication self-efficacy subscale of the Partner

Communication History Scale, as described above (Wingood & DiClemente, 1998b).

Affective attachments and social norms. The Affective attachments and social norms latent variable represented the acquired risks (i.e., social exposures) associated with the Structure of Affective Attachment and Social Norms. Having older sexual partners was measured using the item, “In general how old are the people you have sex with, are they...” Responses included:

1-Much younger than you (4 or more years); 2-Younger than you (2-3 years); 3-About the same age; 4- Older than you (2-3 years); 5- Much older than you (4 or more years). Peer norms for risky sexual behavior was measured as described above.

Knowledge based personal risk factors. The Knowledge based personal risk latent variable represented risk factors (i.e., personal risk factors) associated with the Structure of

Affective Attachments and Social Norms. STD knowledge was measured using the STD

Knowledge Scale (Sikkema et al., 2000). An example item is, “If a man pulls out before orgasm

(cumming), condoms don't need to be used to protect against HIV” (1-True; 2-False; 3-Don’t know). Higher scores indicated greater knowledge. Cronbach’s = .68, indicating questionable

internal consistency (D. George & Mallery, 2003). Reliability analyses indicated that removal of items did not improve internal consistency; as such, all items were retained.

Affective personal risk factors. The Affective personal risk latent variable represented risk factors (i.e., personal risk factors) associated with the Structure of Affective Attachments and Social Norms. Depression was measured using an eight-item version of the Center for

Epidemiological Studies depression symptomology scale-revised (CESD-R; Eaton et al., 2004).

Higher scores on this measure indicated greater levels of depression. Cronbach’s = .91, indicating excellent internal consistency (D. George & Mallery, 2003).

Data Analytic Approach

Preliminary Data Examination

Data were examined in SPSS v. 23 (IBM Corp, 2015). Descriptive, regression, moderation and mediation analyses to test the hypotheses proposed to address research questions in Aims 1a, 1b, 1c, and 2 were conducted in SPSS v. 23 (IBM Corp, 2015). Confirmatory factor analyses (CFA) and structural equation modeling (SEM; Aim 3) were conducted using Mplus v.

7.4 (Muthén & Muthén, 2014).

Continuous and count variables were inspected (via histograms) for the presence of outliers, skew, kurtosis and normality to ensure the normality assumption was met. Substantial departure from normality was indicated by an absolute skew value > 2 and/or an absolute kurtosis value > 4 (Kim, 2013; West, Finch, & Curran, 1995). Outliers were adjusted if they introduced significant bias to the data, as indicated by a z-score > 3.29 (Schroder, Carey, &

Vanable, 2003). Outliers exceeding the 3.29 z-score cut-off were adjusted for all of the core continuous variables; extreme outliers were truncated by giving them a value that is one unit larger than the next most extreme score in the distribution (Schroder et al., 2003; Tabachnick,

Fidell, & Osterlind, 2001) to reduce their influence. The following variables were outlier truncated using this approach: number of lifetime sexual partners; number of sexual partners in past 3 months; fear of condom negotiation; peer norms for risky sexual behavior; power in relationships; and sex refusal self-efficacy. The truncated fear of condom negotiation variable was also log transformed, as inspection of this variable indicated positive skew (skew= 2.52) after truncating the variable. Due to the distribution being highly zero-inflated, the continuous variable, proportion condom use for anal sex in the past three months, was recoded as a dichotomous variable; with 0=no condom use for anal sex and 1=some condom use for anal sex.

Prior to conducting primary study aim analyses all continuous variables were converted to z- scores to ease interpretation of the parameters from the statistical models.

Descriptive Statistics

Sample means and standard deviations for continuous and count variables (demographic, sexual risk, mediators, and TGP constructs) were computed. Sample frequencies and corresponding percentages for categorical demographic, sexual risk, and TGP variables were also computed. In addition, descriptive statistics (mean, standard deviation) to characterize working model-self and -other scores among each of the dominant attachment styles (fearful, preoccupied, secure, dismissive; determined by the item rated by an individual as most characteristic of them) were computed.

Bivariate correlations (Pearson’s r) between the following sociodemographic variables:

(a) age, (b) employment status, (c) government assistance received and the sexual risk outcome variables were analyzed to identify potential covariates to include in analyses for all aims. The correlation between level of intoxication at most recent sexual encounter involving alcohol and condom use at most recent sexual encounter involving alcohol was also examined, as research

indicates that level of intoxication is associated with cognitive impairment and behavioral disinhibition (Peterson, Rothfleisch, Zelazo, & Pihl, 1990), which may affect condom use behaviors (Fromme, D'Amico, & Katz, 1999). Bivariate correlations between sociodemographic variables and sexual risk outcomes with p < .10 identified covariates for inclusion in the analyses. Additionally, age was included as a covariate in all analyses for all aims, as increases in age are consistently associated with greater sexual risk (Sales, Brown, et al., 2012).

Aim 1a

Hypothesis 1.1. Lower working model-self scores (indicative of less favorable self- regard; view that one is unlovable and unworthy of respect) will be associated with: (a) lower proportion of condom use for vaginal sex, (b) higher likelihood of never using condoms for anal sex (past 3 months), (c) higher number of sexual partners (lifetime and past 3 months), (d) higher likelihood of having a casual sexual partner (past 3 months), and (e) higher likelihood of alcohol use before having sex (past 3 months).

Higher working model-other scores (indicative of more favorable other-regard; looking to others for acceptance; view that others are trustworthy) will be associated with: (a) lower proportion of condom use for vaginal sex, (b) higher likelihood of never using condoms for anal sex (past 3 months), (c) higher number of sexual partners (lifetime and past 3 months), (d) higher likelihood of having a casual sexual partner (past 3 months), and (e) higher likelihood of alcohol use before having sex (past 3 months).

Following the data analytic strategies employed by previous studies that examined the relationship between attachment dimensions and risky sexual behavior (Ciesla et al., 2004; J. A.

Feeney et al., 2000; Olley, 2010), a series of multiple regression models were used to determine the association between working model-self (WMS) and working model-other (WMO) scores

(IVs) and the sexual risk outcome variables of interest (a-e). The rationale for including both working models in the same regression model is that, conceptually, attachment is the confluence of these two dimensions, and although these dimensions are separate, they coexist by nature. In the following regression analyses (Aim 1a-Aim 2), the predictors and covariates were standardized using z-scores. The sexual risk outcome variables were not standardized for the following reasons: proportion condom use for vaginal sex, number of sexual partners (lifetime), and number of sexual partners (3 months) were treated as count variables; condom use for anal sex, having casual partners in past 3 month, and frequency of consuming alcohol before sexual intercourse were categorical.

Proportion condom use vaginal sex (3 months). Exploratory analyses indicated that the distribution of proportion condom use for vaginal sex was zero-inflated. Additionally, descriptive statistics for proportion condom use indicated possible overdispersion (M = .21, SD =

0.37), thus a zero-inflated negative binomial multiple regression model with proportion condom use for vaginal sex (past 3 months) as the outcome variable, and WMS and WMO as the predictors was assessed. For these analyses, this variable was multiplied by 100 and rounded to the nearest integer to create a percentage value. The covariates (age, government assistance) and predictors (WMS, WMO) were entered into the model simultaneously.

Condom use for anal sex (past 3 months). A multiple binary logistic model was used to predict the dichotomized condom use for anal sex (3 months) variable (0: never used a condom for anal sex in the past 3 months; 1: used a condom for anal sex at least sometimes during the past 3 months). For this logistic model, the covariate (age) and the predictors (WMS, WMO) were entered simultaneously.

Number of sexual partners (lifetime). Descriptive statistics for number of lifetime sexual partners (M = 12.83, SD = 18.48) suggested the presence of overdispersion. As such, a negative binomial model to predict number of sexual partners (lifetime) was assessed. The covariate (age) and predictors (WMS, WMO) were entered simultaneously.

Number of sexual partners (3 months). Descriptive statistics for number of sexual partners during the past 3 months (M = 2.36, SD = 3.03) suggested the presence of overdispersion. As such, a negative binomial model predicting number of sexual partners (past 3 months) was assessed. The covariate (age) and predictors (WMS, WMO) were entered simultaneously.

Casual sexual partners (3 months). A multiple binary logistic model was used to predict having casual sexual partners in the past 3 months (0: no, 1: yes). For this model, the covariate

(age) and the predictors (WMS, WMO) were entered simultaneously.

Frequency of alcohol use before sex (3 months). Multiple ordinal logistic regression was used to determine the association between WMS and WMO and the dependent variable, frequency of alcohol use before sex in the past 3 months (0: never; 1: almost never; 2: sometimes; 3 almost always; 4: always). For this model, the covariates (age, government assistance) and the predictors (WMS, WMO) were entered simultaneously.

Aim 1b

An additional aim of this study was to explore whether the presence of WMS in the multiple regression models impacted the effect of WMO on the sexual outcome variables of interest; and similarly, whether the presence of WMO in the multiple regression models impacted the effect of WMS on the sexual outcome variables of interest. Furthermore, to determine whether one of the working models (either WMS or WMO) was a stronger predictor

of the sexual risk behaviors (a-e) described above. Given the exploratory nature, no specific hypotheses were provided.

The associations between WMS and each of the sexual outcome variables of interest described above (controlling for covariates) were analyzed using a series simple of regression models, with WMS as the only predictor variable. Similarly, the associations between WMO and each of the sexual outcome variables of interest (controlling for covariates) were analyzed using a series of simple regression models with WMO as the only predictor variable. Note that the type of regression model (e.g., zero-inflated negative binomial, negative binomial, logistic, ordinal) used to predict a particular sexual risk outcome in Aim 1b analyses corresponded with the type of regression model used to predict that outcome variable in the multiple regression analyses

(Aim 1a). Also note that the covariates that were included in Aim 1b regression models for a particular sexual outcome were consistent with covariates that were included in the multiple regression model for that particular sexual risk outcome variable (Aim 1a). As with Aim 1a analyses, the Aim 1b regression models were conducted with the predictors and covariates standardized using z-scores. Lastly, as with Aim 1a, the sexual risk outcome variables were not standardized in the Aim 1b regression models.

Proportion condom use vaginal sex (3 months). Two separate zero-inflated negative binomial regression models were used to predict proportion condom use for vaginal sex (past 3 months). For these analyses, this variable was multiplied by 100 and rounded to the nearest integer to create a percentage value. In the first zero-inflated negative binomial regression model, the covariates (age, government assistance) and the predictor (WMS) were entered into the model simultaneously. For the second zero-inflated negative binomial regression model, the

covariates (age, government assistance) and the predictor (WMO) were entered into the model simultaneously.

Condom use for anal sex (past 3 months). Two separate binary logistic models were used to predict the dichotomized condom use for anal sex (3 months) variable (0: never used a condom for anal sex in the past 3 months; 1: used a condom for anal sex at least sometimes during the past 3 months). For the first logistic model, the covariate (age) and the predictor

(WMS) were entered simultaneously. For the second logistic model, the covariate (age) and the predictor (WMO) were entered simultaneously.

Number of sexual partners (lifetime). Two separate negative binomial models were used to predict number of lifetime sexual partners. For the first negative binomial model, the covariate (age) and predictor (WMS) were entered simultaneously. For the second negative binomial model, the covariate (age) and predictor (WMO) were entered simultaneously.

Number of sexual partners (3 months). Two separate negative binomial models were used to predict number of sexual partners during the past 3 months. For the first model, the covariate (age) and predictor (WMS) were entered simultaneously. For the second model, the covariate (age) and predictor (WMO) were entered simultaneously.

Casual sexual partners (3 months). Two separate binary logistic models were used to predict having casual sexual partners in the past 3 months (0:no; 1: yes). For the first model, the covariate (age) and the predictor (WMS) were entered simultaneously. For the second model, the covariate (age) and the predictor (WMO) were entered simultaneously.

Frequency of alcohol use before sex (3 months). Two separate ordinal logistic regression models were used to predict frequency of alcohol use before sex during the past 3 months (0: never; 1: almost never; 2: sometimes; 3: almost always; 4: always). For the first

model, the covariates (age, government assistance) and the predictor (WMS) were entered simultaneously. For the second model, the covariates (age, government assistance) and the predictor (WMO) were entered simultaneously.

The results of the Aim 1b simple regression analyses were compared to the results from the multiple regression models from Aim 1a analyses. This was done to determine whether the effect of a working model (Aim 1b) became statistically non-significant in the presence of the other working model (Aim 1a). For example, this would have been indicated if (1) the effect of

WMO on number of lifetime sexual partners was significant in the model with WMO as the only predictor variable; (2) the effect of WMS on number of lifetime sexual partners was significant in the model with WMS as the only predictor variable; and (3) the effect of WMO on number of sexual partners was no longer significant in the multiple regression model, however the effect of

WMS remained significant in the multiple regression model with both WMS and WMO included as predictor variables. This would suggest that WMS better accounted for variance in number of sexual partners above and beyond what could be accounted for by WMO.

It was proposed that in the event that both WMS and WMO other were significant in the multiple regression model, the standardized partial regression coefficient of WMS would be compared to the standardized partial regression coefficient of WMO to determine if the effect of one working model was significantly different than the effect of the other working model.

Specifically, it was proposed that t-test analyses would be used to determine if there was a significant difference between the standardized partial regression coefficient of WMS and the standardized partial regression coefficient of WMO, according to the procedures described by

Cohen, Cohen, Aiken, and West, (2003). As there were no multiple regression models in which both WMS and WMO were significant predictors, these t-test analyses were not conducted.

Aim 1c

Hypothesis 1.2b. Partner type will moderate the relationship between working model- self scores and condom use at the most recent sexual encounter not involving alcohol such that working model-self scores will have a stronger positive effect on condom use with a steady partner at the most recent sexual encounter not involving alcohol than with a casual partner at the most recent sexual encounter not involving alcohol.

Partner type at most recent sexual encounter not involving alcohol was recoded as follows: the response (1: someone I just met) was recoded as (0: casual partner); the response (2: a casual partner) was recoded as (0: casual partner); the response (3: my boyfriend) was recoded as (1: non-casual partner). Partner type at the most recent sexual encounter involving alcohol was recoded in the same manner.

A hierarchical binary logistic regression model with condom use at most recent sexual encounter not involving alcohol as the dependent variable was analyzed. The covariates (age, government assistance) and the main effects of partner type at most recent sexual encounter not involving alcohol, WMS, and WMO were entered simultaneously in step one; the interaction

between WMS and partner type and the interaction between WMO and partner type were entered in step two. As was the case for Aim 1a and Aim 1b, this moderation analysis was conducted with WMS and WMO and the covariates standardized using z-scores. Being that the outcome variable (condom use at most recent sexual encounter not involving alcohol) and the moderator

(partner type) were dichotomous, they were not standardized.

Hypothesis 1.3b. Partner type will moderate the relationship between working model- self scores and condom use at the most recent sexual encounter involving alcohol such that working model-self scores will have a stronger positive effect on condom use with a steady partner at the most recent sexual encounter involving alcohol than with a casual partner at the most recent sexual encounter involving alcohol.

Hypothesis 1.3c. Partner type will moderate the relationship between working model- other scores and condom use at the most recent sexual encounter involving alcohol such that working model-other scores will have a stronger negative effect on condom use with a casual partner at the most recent sexual encounter involving alcohol than with a steady partner at the most recent sexual encounter involving alcohol.

Condom use at most recent sexual encounter involving alcohol was recoded such that (2: no) and (3: do not remember) were recoded as (0: did not use a condom or do not remember if used a condom); (1: yes) remained coded as (1: used a condom). A hierarchical binary logistic regression model with condom use at most recent sexual encounter involving alcohol as the dependent variable was analyzed. In addition to the sociodemographic covariate age, level of

intoxication at most recent sexual encounter involving alcohol was controlled for as a covariate, as bivariate analyses indicated a correlation (r = .209, p < .001) between level of intoxication at most recent sexual encounter involving alcohol and condom use at most recent sexual encounter involving alcohol. The covariates (age, level of intoxication at most recent sexual encounter involving alcohol) and the main effects of partner type at most recent sexual encounter involving alcohol, WMS, and WMO were entered simultaneously in step one; the interaction between

WMS and partner type and the interaction between WMO and partner type were entered in step two. As was the case for Aim 1a and Aim 1b, this moderation analysis was conducted with

WMS and WMO and age standardized using z-scores. Being that the outcome variable (condom use at most recent sexual encounter involving alcohol), the moderator (partner type), and the covariate (level of intoxication) were categorical, they were not standardized.

Aim 2

Mediation models testing Aim 2 hypotheses were estimated using the PROCESS macro for SPSS (Hayes, 2013). PROCESS provided estimates of the path coefficients for total, direct and indirect effects using ordinary least squares or logistic regression. Using PROCESS, bootstrapped 95% confidence intervals for indirect effects were calculated based on 1,000 bootstrap re-samples. According to the Baron and Kenny (1986) method, full mediation was determined by: 1) the total effect of the independent variable on the dependent variable (path c) being significant (p < .05); 2) the effect of the independent variable on the mediator (path a) being significant (p < .05); 3) the effect of the mediator on the outcome variable (path b) being significant (p < .05); 4) the direct effect (path c’) having a p-value > .05 in the mediated model; and the additional criteria 5) the 95% bootstrapped confidence interval for the indirect effect

(a*b) not including zero, based on the product of coefficients method (Shrout, 2002) . Partial

mediation was determined by: 1) the total effect of the independent variable on the dependent variable (path c) being significant (p < .05); 2) the effect of the independent variable on the mediator (path a) being significant (p < .05); 3) the effect of the mediator on the outcome variable (path b) being significant (p < .05); 4) the direct effect having a p-value < .05 in the mediated model (path c’); and 5) the 95% bootstrapped confidence interval for the indirect effect

(a*b) not including zero. Establishment of an indirect effect of WMS or WMO on the sexual risk outcome variable through the mediator was based on the following criteria: (a) the indirect effect

(a*b) was significantly non-zero. Note that it is not a requirement that the independent variable have a significant zero-order effect on the outcome variable for there to be an indirect effect of X on Y (Hayes, 2009).

Sociodemographic variables identified as having bivariate correlations with p > .10 with sexual risk outcome variables were included in the mediation models as covariates. For all of the mediation analyses conducted using PROCESS, the analyses were conducted with the covariates, predictors, and mediators standardized using z-scores. Note that PROCESS does not create zero- inflated negative binomial or negative binomial mediation models; as such, proportion condom use for vaginal sex (3 months) was treated as a continuous variable, and was standardized using z-scores.

self-efficacy will be positively associated with a lower proportion of condom use for vaginal and anal sex.

Using the PROCESS macro, WMS was the independent variable, WMO was a covariate

(as a proxy for a second independent variable in the mediation model), age and government assistance were sociodemographic covariates, partner communication self-efficacy was the mediator, and proportion condom use for vaginal sex (past 3 months) was the dependent variable. In a separate analysis, the dependent variable was changed to the dichotomous condom use for anal sex (past 3 months) variable; keeping all other variables the same (however, note that age was the only sociodemographic covariate in the model with condom use for anal sex as the dependent outcome variable).

The independent variable was then changed to WMO, with WMS as a covariate, age and government assistance were sociodemographic covariates, partner communication self-efficacy was the mediator, and proportion condom use for vaginal sex (past 3 months) was the dependent variable. In a separate analysis, the dependent variable was changed to the dichotomous condom use for anal sex (past 3 months) variable; keeping all other variables the same (however, note that age was the only sociodemographic covariate in the model with condom use for anal sex as the dependent outcome variable).

The total, direct and indirect effects for WMS when it was the independent variable (with

WMO as a covariate) were used to determine the presence of mediation/indirect effect in the

WMS mediated model. Similarly, the total, direct and indirect effects for WMO when it was the independent variable (with WMS as a covariate) were used to determine the presence of mediation/indirect effect in the WMO mediated model.

Using the PROCESS macro, WMS was the independent variable, WMO was a covariate

(as a proxy for a second independent variable in the mediation model), age and government assistance were sociodemographic covariates, fear of condom negotiation was the mediator, and proportion condom use for vaginal sex (past 3 months) was the dependent variable. In a separate analysis, the dependent variable was changed to the dichotomous condom use for anal sex (past 3 months) variable; keeping all other variables the same (however, note that age was the only sociodemographic covariate in the model with condom use for anal sex as the dependent outcome variable).

The independent variable was then changed to WMO, with WMS as a covariate, age and government assistance were sociodemographic covariates, fear of condom negotiation was the mediator, and proportion condom use for vaginal sex (past 3 months) was the dependent variable. In a separate analysis, the dependent variable was changed to the dichotomous condom use for anal sex (past 3 months) variable; keeping all other variables the same (however, note that age was the only sociodemographic covariate in the model with condom use for anal sex as the dependent outcome variable).

The total, direct and indirect effects for WMS when it was the independent variable (with

WMO as a covariate) were used to determine the presence of mediation/indirect effect in the

Hypothesis 2.3. Peer norms for risky sexual behaviors will partially mediate the relationship between working model-self and -other scores and proportion of condom use for vaginal and anal sex (past 3 months). Lower working model-self scores will be negatively associated with greater perceptions of peer norms for risky sexual behaviors; higher working model-other scores will be positively associated with greater perceptions of peer norms for risky sexual behavior. Greater perceptions of peer norms for risky sexual behavior will be negatively associated with a lower proportion of condom use for vaginal and anal sex.

Using the PROCESS macro, WMS was the independent variable, WMO was a covariate

(as a proxy for a second independent variable in the mediation model), age and government assistance were sociodemographic covariates, peer norms for risky sexual behavior was the mediator, and proportion condom use for vaginal sex (past 3 months) was the dependent variable. In a separate analysis, the dependent variable was changed to the dichotomous condom use for anal sex (past 3 months) variable; keeping all other variables the same (however, note that age was the only sociodemographic covariate in the model with condom use for anal sex as the dependent outcome variable).

The independent variable was then changed to WMO, with WMS as a covariate, age and government assistance were sociodemographic covariates, peer norms for risky sexual behavior was the mediator, and proportion condom use for vaginal sex (past 3 months) was the dependent

variable. In a separate analysis, the dependent variable was changed to the dichotomous condom use for anal sex (past 3 months) variable; keeping all other variables the same (however, note that age was the only sociodemographic covariate in the model with condom use for anal sex as the dependent outcome variable).

The total, direct and indirect effects for WMS when it was the independent variable (with

WMO as a covariate) were used to determine the presence of mediation/indirect effect in the

WMS mediated multiple model. Similarly, the total, direct and indirect effects for WMO when it was the independent variable (with WMS as a covariate) were used to determine the presence of mediation/indirect effect in the WMO mediated model.

Hypothesis 2.4. Partner trust levels will partially mediate the relationship between working model-self and -other scores and condom use with boyfriend or main partner at last sexual encounter with boyfriend/main partner. The nature of the relationship between working model-self and partner trust remains to be determined, therefore no specific hypothesis is provided regarding the relationship between level of partner trust and working model-self. Higher working model-other scores will be positively associated with higher levels of partner trust.

Higher levels of partner trust will be negatively associated with likelihood of using condoms with a boyfriend or main partner at most recent sexual encounter with boyfriend or main partner among women in a steady relationship.

Using PROCESS macro, WMS was the independent variable, WMO was a covariate (as a proxy for a second independent variable in the mediation model), age was the sociodemographic covariate, partner trust was the mediator, and condom use with boyfriend or main partner at most recent sexual encounter with boyfriend or main partner was the dependent variable.

The independent variable was then changed to WMO, with WMS as a covariate, age was the sociodemographic covariate, partner trust was the mediator, and condom use with boyfriend or main partner at most recent sexual encounter with boyfriend or main partner was the dependent variable.

The total, direct and indirect effects for WMS when it was the independent variable (with

WMO as a covariate) were used to determine the presence of mediation/indirect effect in the

Hypothesis 2.5. Sex-related alcohol expectancies will partially mediate the relationship between working model-self and –other scores and frequency of having sex while intoxicated.

Because alcohol use is associated with not using condoms during sexual encounters that occur during or after consuming alcohol, the sexual risk outcome variable, frequency of having sex while intoxicated was dichotomized to indicate no engagement in sex after using substances

(1: Never) versus some engagement in sex while intoxicated (0: all other responses [almost never, sometimes, almost always, always]).

Within the PROCESS macro, WMS was the independent variable, WMO was a covariate

(as a proxy for a second independent variable in the mediation model), age and government

assistance were sociodemographic covariates, sex-related alcohol expectancies was the mediator, and the dichotomous frequency of having sex while intoxicated variable (never vs. all other responses) was the dependent variable.

The independent variable was then changed to WMO, with WMS as a covariate, age and government assistance were sociodemographic covariates, sex-related alcohol expectancies was the mediator, and the dichotomous frequency of having sex while intoxicated variable (never vs. all other responses) was the dependent variable.

The total, direct and indirect effect for WMS when it was the independent variable (with

WMO as a covariate) were used to determine the presence of mediation/indirect effect in the

WMS mediated model. Similarly, the total, direct and indirect effect for WMO when it was the independent variable (with WMS as a covariate) were used to determine the presence of mediation/indirect effect in the WMO mediated model.

Aim 3

Hypothesis 3.1. The TGP conceptualization of HIV-risk among African American women with the added construct of attachment will demonstrate good fit as a model of sexual

Assessment of measurement models. As per DePadilla et al. (2011)’s methodology, the latent Sexual division of labor variable was calculated by summing its proposed indicators

(education; employment status; and government assistance received). Government assistance received was dichotomized such that 0= no assistance received and 1= any form of government assistance received. Education level was dichotomized as well: 0= high school or greater; 1= less

than high school. Employment status was recoded such that 0=employed and 1= not employed.

Greater composite scores indicated greater acquired risk associated with the sexual division of labor. A series of confirmatory factor analyses (CFA) were conducted using Mplus v. 7.4

(Muthén & Muthén, 2014) to assess the proposed measurement models for each latent variable

(see Figure 3). Standardized path coefficients between latent constructs and their indicator variables were estimated. Indicator variables with path coefficients that were non-significant were removed from the measurement model, and the CFA was estimated again, including only the indicators with significant path coefficients. Results of the final measurement model

(excluding non-significant paths) for each latent construct are presented in the results section. In order to provide more stability in the measurement models, the individual scale items that composed a total scale score (e.g., the individual items that were summed to calculate a total fear of condom negotiation score) were used in place of the single total scores (e.g., the total score on fear of condom negotiation scale). As such, the indicator variables for the latent constructs included in the current CFA analyses were as follows:

Attachment. Working model-self (WMS) and working model-other (WMO) were included as indicator variables in the CFA of the measurement model for the Attachment latent construct.

Affective attachment and social norms. The CFA for the measurement model for this latent construct included how old participants’ male sexual partners typically are, and the five items of the Peer Norms for Risky Sexual Behavior Scale as indicator variables (PN1-PN5; measured on ordinal scale).

Sexual division of power. The CFA for the measurement model for this latent construct included the Control subscale items of the Power in Relationships Scale (PRC1-PRC9; measured

on ordinal scale); the Fear of Condom Negotiation Scale items (FCN1-FCN7; measured on ordinal scale); having ever been forced to have vaginal sex; having ever been physically abused; and having ever been emotionally abused.

Affective personal risks. The CFA for the measurement model for this latent construct included the CESD-R items, measuring depression (D1-D8; measured on ordinal scale).

Knowledge based personal risks. The CFA for the measurement model for this latent construct included the STD Knowledge Scale items (KNW1-KNW11; measured on ordinal scale).

Behavioral Risk. The CFA for the measurement model for this latent construct included the partner communication self-efficacy subscale items (PCSE1-PCSE6; measured on ordinal scale) and the partner communication frequency subscale items (PCF1-PCF4) of the Partner

Communication History Scale. It also included the Sex-Refusal Self-Efficacy Scale items

(SRSE1-SRSE7; measured on ordinal scale), and an index of substance use during sexual encounters.

Sexual HIV/STI risk behaviors. The CFA for the measurement model for this latent construct included proportion condom use for vaginal sex (past 3 months), number of lifetime sexual partners, number of sexual partners in past 3 months, condom use at most recent sexual encounter involving alcohol, and condom use at most recent sexual encounter not involving alcohol.

Assessment of the proposed structural model. Upon establishing measurement models with significant standardized path coefficients, the associations between latent constructs were tested using structural equation modeling (SEM) in Mplus v. 7.4 (Muthén & Muthén, 2014).

Model building procedures were used to assess the proposed structural model (see Figure 4). A

series of SEM analyses were used to test portions of the proposed model by estimating standardized path coefficients. The procedure began by separately testing the proposed paths between the Attachment latent construct and the following TGP latent constructs: (a) Sexual division of power, (b) Affective attachment and social norms, (c) Affective personal risks, (d) behavioral risk, and (e) Sexual HIV/STI risk behaviors. Note that in each of these models,

Attachment was the predictor variable and the TGP latent construct was the outcome variable; further, age was included as a covariate only if it was associated with the TGP outcome variable.

The criteria used to determine whether Attachment was associated with a particular TGP latent construct included: a significant standardized regression coefficient between the latent constructs and good model fit. When applicable, model fit was assessed using the chi-square difference test, chi-square goodness of fit test, the comparative fit index (CFI), and/or root mean square error of approximation (RMSEA); a CFI close to .95 or greater, an RMSEA close to .06 or less, a significant chi-square difference test, and a non-significant chi-square goodness of fit test indicate good model fit (Muthén & Muthén, 2014). Models that indicated good model fit and significant standardized path coefficients were assessed with age included as a covariate.

Age was retained in the model if it was significantly associated with the TGP outcome variable.

Assessment of model fit. It was proposed that upon establishing which TGP latent constructs were associated with Attachment, these portions of the model would be combined and model fit assessed. However, as noted below in the Results section, models including

Attachment and more than one TGP latent construct were not assessed because the association between Attachment and Sexual STI/HIV risk behaviors--the central and novel aspect of the proposed model--did not indicate good model fit. As such, the results presented are the association between Attachment and individual TGP latent constructs.

Results

Descriptive Univariate Analyses

WMS (M= 0.79, SD = 2.11) and WMO (M = 0.15, SD = 2.07) scores ranged from -6

(unfavorable view of self/others) to +6 (favorable view of self/other). In terms of dominant attachment style, nearly half of the sample (49.82%) was classified as fearful, as per Ciesla et al.’s (2004) scoring guidelines. Table 2 presents the means and standard deviations for WMS and

WMO scores by dominant attachment style.

Descriptive statistics characterizing participants’ sexual risk behaviors are presented in

Table 3. The average proportion of condom use for anal sex during the past 3 months (M = .21,

SD = 0.37), as well as the average proportion of condom use for vaginal sex during the past 3 months (M = .33, SD = 0.31) were both low. Of women reporting anal sex in the past 3 months

(N=101), 68.32% never used a condom for anal sex during the past 3 months. Regarding specific sexual encounters, over half (65.78%) of the sample who reported ever having consumed alcohol before having sex (N= 412) did not use a condom at most recent sexual encounter involving alcohol, and the majority of the entire sample (64.29%) did not use a condom at most recent sexual encounter not involving alcohol. Among women who reported having a boyfriend or main sexual partner (N= 462), the majority (75.76%) did not use a condom at most recent sexual encounter with boyfriend or main partner. Number of lifetime sexual partners ranged from 1 to

200 (M = 12.83, SD = 18.48). Likewise, there was a wide range for number of sexual partners in past 3 months (Range: 1-34, M = 2.36, SD = 3.03). Nearly half (44.11%) of the sample reported having at least 1 casual sexual partner in the past 3 months; moreover, 30.18% had a casual partner at most recent sexual encounter not involving alcohol, and 33.01% of the sample who reported ever having consumed alcohol before having sex (N= 412) had a casual partner at most

recent sexual encounter involving alcohol. Regarding frequency of consuming alcohol before having sexual intercourse during the past 3 months, 26.43% of the sample responded “never,”

17.14% “almost never,” 40.00% “sometimes,” 11.07% “almost always,” and 5.36% “always.”

Descriptive information regarding the following TGP constructs is presented in Table 4: partner trust, fear of condom negotiation, partner communication self-efficacy, peer norms for risky sexual behavior, sex-related alcohol outcome expectancies, relationship power, HIV/STD knowledge, depression, partner communication frequency, sex refusal self-efficacy, coerced sex, history of physical abuse, history of emotional abuse, and age of sexual partners.

Aim 1a Multiple Regression Analyses

Proportion condom use vaginal sex (3 months). Results from the multiple zero-inflated negative binomial regression model indicated that the overdispersion parameter was significant

(dispersion = 0.84, 95% CI: 0.69, 0.99), confirming that a zero-inflated negative binomial model was most appropriate for the data. Results from the zero-inflated negative binomial multiple regression model with proportion condom use for vaginal sex as the outcome variable indicated that neither WMS nor WMO were associated with proportion condom use for vaginal sex over the past 3 months (see Table 5, Model 1).

Condom use for anal sex (past 3 months). Table 5, Model 2 presents the results from the multiple binary logistic regression model predicting odds of having used a condom for anal sex during the past 3 months. Results indicated that neither WMS nor WMO were associated with the likelihood of having used a condom for anal sex over the past 3 months.

Number of sexual partners (lifetime). Negative binomial regression analyses indicated that the overdispersion parameter for number of lifetime sexual partners was significant

(dispersion = 0.66, 95% CI: 0.59, 0.75). Results from the negative binomial multiple regression

model predicting number of lifetime sexual partners are presented in Table 5, Model 3. Results indicated that WMS was a significant predictor of number of the lifetime sexual partners; for every one standard deviation decrease in WMS score, number of lifetime sexual partners increased by 0.84 (p < .001). As depicted in Table 5, Model 3, WMO was not associated with number of the lifetime sexual partners.

Number of sexual partners (3 months). Negative binomial regression analyses indicated that the overdispersion parameter for number of sexual partners in past 3 months was significant (dispersion = 0.23, 95% CI: 0.17, 0.31). Results from the negative binomial multiple regression model predicting number of sexual partners in the past 3 months are presented in

Table 5, Model 4. Results indicated that WMS was a significant predictor of the number of sexual partners in the past 3 months; for every one standard deviation decrease in WMS score, number of lifetimes sexual partners increased by 0.85 (p < .001). WMO was not associated with number of sexual partners during the past 3 months

Casual sexual partners (3 months). Table 5, Model 5 presents the results from the multiple binary logistic model predicting odds of having a casual sexual partner(s) during the past 3 months. Neither WMS nor WMO were associated with likelihood of having one or more casual sexual partners during the past 3 months.

Frequency of alcohol use before sex (3 months). Results from the multiple ordinal logistic model predicting frequency of consuming alcohol before sexual encounters during the past 3 months are presented in Table 5, Model 6. Neither WMS nor WMO were associated with frequency of consuming alcohol prior to engaging in sexual activity during the past 3 months.

Aim 1b Simple Regression Analyses

Proportion condom use for vaginal sex (3 months). Table 6, Model 1 and Table 7,

Model 1 present the results from the simple zero-inflated negative binomial models predicting proportion condom use for vaginal sex (past 3 months). WMS was not associated with proportion condom use for vaginal sex (past 3 months) when the effect of WMO was not included in the model (see Table 6, Model 1). Similarly, WMO was not associated with proportion condom use for vaginal sex (past 3 months) when the effect of WMS was not included in the model (see Table 7, Model 1). These results corresponded with results from the multiple regression model predicting proportion condom use for vaginal sex (past 3 months), in which neither WMS nor WMO were significant predictors of proportion condom use for vaginal sex.

Condom use for anal sex (past 3 months). Results from the simple binary logistic models predicting condom use for anal sex during the past 3 months indicated that WMS was not a significant predictor of condom use for anal sex when the effect of WMO was not accounted for in the model (see Table 6, Model 2). Similarly, WMO was not a significant predictor of condom use for anal sex when WMS was not included in the model as a predictor (see Table 7,

Model 2). These results corresponded with the non-significant results from the multiple binary logistic regression model including both WMS and WMO as predictors of condom use for anal sex during the past 3 months.

Number of sexual partners (lifetime). Similar to the results from the multiple regression analysis, results from the simple negative binomial regression analyses indicated that

WMS was a significant predictor of the number of lifetime sexual partners; for every one standard deviation decrease in WMS score, the number of lifetime sexual partners increased by

0.84 (p < .001; see Table 6, Model 3). These findings suggested that accounting for the effect of

WMO did not impact the relationship between WMS and number of lifetime sexual partners. As shown in Table 7, Model 3, WMO was not associated with number of lifetime sexual partners when the effect of WMS was not included in the model; this was consistent with the results from the multiple regression analysis in which both WMS and WMO were included in the model predicting number of lifetime sexual partners.

Number of sexual partners (3 months). Table 6, Model 4 and Table 7, Model 4 present the results from the simple negative binomial models predicting the number of sexual partners in the past 3 months. WMS was a significant predictor of number of sexual partners in the past 3 months; for every one standard deviation decrease in WMS score, number of sexual partners increased by 0.85 (p < .001; see Table 6, Model 4). These findings suggested that accounting for the effect of WMO did not impact the relationship between WMS and number of sexual partners during the past 3 months. As indicated in Table 7, Model 4, WMO was not associated with number of sexual partners when WMS was not included in the model; this was consistent with the results from the multiple regression analysis in which both WMS and WMO were included in the model predicting number of sexual partners in the past 3 months.

Casual sexual partners (3 months). Results from the simple binary logistic models predicting the odds of having at least 1 casual sexual partner during the past 3 months are presented in Table 6, Model 5 and Table 7, Model 5. As indicated in Table 6, Model 5, WMS was a significant predictor of the odds of having a casual sexual partner(s) in the past 3 months; for every one standard deviation increase in WMS score, the odds of having casual sexual partners in the past 3 months decreased by 16% (p = .036). However, results from the multiple regression analysis in Aim 1a suggested that when accounting for the effect of WMO, the effect

of WMS on the odds of having a casual sexual partner(s) was not significant. WMO was not associated with having any casual sexual partners during the past 3 months when the effect of

WMS was not included in the model (see Table 7, Model 5). This was consistent with the results from the multiple regression analysis predicting likelihood of having any casual sexual partners during the past 3 months; WMO was not associated with having casual partners when the effect of WMS was included in the model.

Frequency of alcohol use before sex (3 months). Results from the simple ordinal logistic models predicting frequency of alcohol use prior to sexual encounters during the past 3 months are presented in Table 6, Model 6 and Table 7, Model 6. WMS was not associated with frequency of alcohol use prior to sexual encounters during the past 3 months when WMO was not included in the model (see Table 6, Model 6). Similarly, WMO was not associated with frequency of alcohol use prior to sexual encounters during the past 3 months when WMS was not included in the model (see Table 7, Model 6). These results corresponded with the non- significant results from the multiple ordinal regression model including both WMS and WMO as predictors of frequency of alcohol use prior to sexual encounters during the past 3 months.

Aim 1c Moderation Analyses

Condom use at most recent sexual encounter involving alcohol. Results from moderation analyses are presented in Table 8. Results presented in Table 8, Model 1 indicate that neither WMO nor WMS were associated with condom use at most recent sexual encounter involving alcohol. In addition, there was a non-significant interaction between WMS and partner type, as well as between WMO and partner type.

Condom use at most recent sexual encounter not involving alcohol. Similarly, neither

WMO nor WMS were associated with condom use at most recent sexual encounter not involving

alcohol (see Table 8, Model 2). In addition, there was a non-significant interaction between

WMS and partner type, as well as between WMO and partner type in predicting the odds of condom use at most recent sexual encounter not involving alcohol.

Aim 2 Mediation Analyses

Figure 5 provides a general representation of the mediation models with WMS as the main predictor variable and WMO as a covariate (as a proxy for a multiple regression model with both WMS and WMO as independent variables). Figure 6 presents a similar representation for the mediation models with WMO as the main predictor variable and WMS as a covariate.

Hypothesis 2.1: Proportion condom use for vaginal sex (past 3 months), mediated by partner communication self-efficacy. Table 9, Model A displays results from the mediation model predicting proportion condom use for vaginal sex (past 3 months), with WMS as the independent variable, WMO as a covariate, and partner communication self-efficacy as the mediator. The total effect of WMS on proportion condom use for vaginal sex was not significant; as such, there was not a relationship to be partially mediated by partner communication self- efficacy. Further, the indirect effect of WMS on proportion condom use for vaginal sex through partner communication self-efficacy was non-significant, as indicated by the 95% confidence interval (CI) for the indirect effect. However, the relationship between WMS and partner communication self-efficacy was significant (p < .001); for every one standard deviation increase in WMS score, partner communication self-efficacy increased by 0.26 standard deviations.

Table 9, Model B displays results from the mediation model predicting proportion condom use for vaginal sex (past 3 months), with WMO as the independent variable, WMS as a covariate, and partner communication self-efficacy as the mediator. The total effect of WMO on proportion condom use for vaginal sex was not significant; as such, there was not a relationship

to be partially mediated by partner communication self-efficacy. Further, the indirect effect of

WMO on proportion condom use for vaginal sex through partner communication self-efficacy was non-significant, as indicated by the 95% CI for the indirect effect.

Hypothesis 2.1: Condom use for anal sex (past 3 months), mediated by partner communication self-efficacy. Table 10, Model C displays results from the mediation model predicting any condom use for anal sex (past 3 months), with WMS as the independent variable,

WMO as a covariate, and partner communication self-efficacy as the mediator. The total effect of WMS on condom use for anal sex was not significant; as such, there was not a relationship to be partially mediated by partner communication self-efficacy. However, the relationship between

WMS and partner communication self-efficacy was significant (p < .001); for every one standard deviation increase in WMS score, partner communication self-efficacy increased by 0.43 standard deviations. Furthermore, the relationship between partner communication self-efficacy and condom use for anal sex was significant; for every 1 standard deviation increase in partner communication self-efficacy, the odds of condom use increased by 64%. The indirect effect of

WMS on any condom use for anal sex through partner communication self-efficacy was significant, as indicated by the 95% CI for the indirect effect.

Table 10, Model D displays results from the mediation model predicting condom use for anal sex (past 3 months), with WMO as the independent variable, WMS as a covariate, and partner communication self-efficacy as the mediator. The total effect of WMO on condom use for anal sex was not significant; as such, there was not a relationship to be partially mediated by partner communication self-efficacy. Further, the indirect effect of WMO on any condom use for anal sex through partner communication self-efficacy was non-significant, as indicated by the

95% CI for the indirect effect. As in Table 10, Model C, the relationship between partner

communication and condom use for anal sex was significant; for every 1 standard deviation increase in partner communication self-efficacy, the odds of condom use increased by 64%.

Hypothesis 2.2: Proportion condom use for vaginal sex (3 months), mediated by fear of condom negotiation. Table 11, Model A displays results from the mediation model predicting proportion condom use for vaginal sex (past 3 months), with WMS as the independent variable,

WMO as a covariate, and fear of condom negotiation as the mediator. The total effect of WMS on proportion condom use for vaginal sex was not significant; as such, there was not a relationship to be partially mediated by partner fear of condom negotiation. Further, the indirect effect of WMS on proportion condom use for vaginal sex through fear of condom negotiation was non-significant, as indicated by the 95% CI for the indirect effect. However, the relationship between WMS and fear of condom negotiation was significant (p < .001); for every one standard deviation increase in WMS score, fear of condom negotiation decreased by 0.23 standard deviations.

Table 11, Model B displays results from the mediation model predicting proportion condom use for vaginal sex (past 3 months), with WMO as the independent variable, WMS as a covariate, and fear of condom negotiation as the mediator. The total effect of WMO on proportion condom use for vaginal sex was not significant; as such, there was not a relationship to be partially mediated by fear of condom negotiation. Further, the indirect effect of WMO on proportion condom use for vaginal sex through fear of condom negotiation was non-significant, as indicated by the 95% CI for the indirect effect.

Hypothesis 2.2: Condom use for anal sex (past 3 months), mediated by fear of condom negotiation. Table 12, Model C displays results from the mediation model predicting condom use for anal sex (past 3 months), with WMS as the independent variable, WMO as a

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covariate, and fear of condom negotiation as the mediator. The total effect of WMS on condom use for anal sex was not significant; as such, there was not a relationship to be partially mediated by fear of condom negotiation. Further, the indirect effect of WMS on any condom use for anal sex through fear of condom negotiation was non-significant, as indicated by the 95% CI for the indirect effect. However, the relationship between WMS and fear of condom negotiation was significant (p = .038); for every one standard deviation increase in WMS score, fear of condom negotiation decreased by 0.26 standard deviations.

Table 12, Model D displays results from the mediation model predicting condom use for anal sex (past 3 months), with WMO as the independent variable, WMS as a covariate, and fear of condom negotiation as the mediator. The total effect of WMO on condom use for anal sex was not significant; as such, there was not a relationship to be partially mediated by fear of condom negotiation. Further, the indirect effect of WMO on condom use for any anal sex through fear of condom negotiation was non-significant, as indicated by the 95% CI for the indirect effect.

Hypothesis 2.3: Proportion condom use for vaginal sex (3 months), mediated by peer norms for risky sexual behavior. Table 13, Model A displays results from the mediation model predicting proportion condom use for vaginal sex (past 3 months), with WMS as the independent variable, WMO as a covariate, and peer norms for risky sexual behavior as the mediator. The total effect of WMS on proportion condom use for vaginal sex was not significant; as such, there was not a relationship to be partially mediated by peer norms for risky sexual behavior. Further, the indirect effect of WMS on proportion condom use for vaginal sex through peer norms for risky sexual behavior was non-significant, as indicated by the 95% CI for the indirect effect.

However, the relationship between WMS and peer norms for risky sexual behavior was

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significant (p < .001); for every one standard deviation increase in WMS score, peer norms for risky sexual behavior decreased by 0.15 standard deviations.

Table 13, Model B displays results from the mediation model predicting proportion condom use for vaginal sex (past 3 months), with WMO as the independent variable, WMS as a covariate, and peer norms for risky sexual behavior as the mediator. The total effect of WMO on proportion condom use for vaginal sex was not significant; as such, there was not a relationship to be partially mediated by peer norms for risky sexual behavior. Further, the indirect effect of

WMO on proportion condom use for vaginal sex through peer norms for risky sexual behavior was non-significant, as indicated by the 95% CI for the indirect effect.

Hypothesis 2.3: Condom use for anal sex (past 3 months), mediated by peer norms for risky sexual behavior. Table 14, Model C displays results from the mediation model predicting condom use for anal sex (past 3 months), with WMS as the independent variable,

WMO as a covariate, and peer norms for risky sexual behavior as the mediator. The total effect of WMS on condom use for anal sex was not significant; as such, there was not a relationship to be partially mediated by peer norms for risky sexual behavior. Further, the indirect effect of

WMS on any condom use for anal sex through peer norms for risky sexual behavior was non- significant, as indicated by the 95% CI for the indirect effect. However, the relationship between

WMS and peer norms for risky sexual behavior was significant (p = .038); for every one standard deviation increase in WMS score, peer norms for risky sexual behaviors decreased by

0.27 standard deviations.

Table 14, Model D displays results from the mediation model predicting condom use for anal sex (past 3 months), with WMO as the independent variable, WMS as a covariate, and peer norms for risky sexual behavior as the mediator. The total effect of WMO on condom use for

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anal sex was not significant; as such, there was not a relationship to be partially mediated by peer norms for risky sexual behavior. Further, the indirect effect of WMO on any condom use for anal sex through peer norms for risky sexual behavior was non-significant, as indicated by the 95%

CI for the indirect effect.

Hypothesis 2.4: Condom use at most recent sexual encounter with boyfriend or main sexual partner, mediated by partner trust. Table 15, Model A displays results from the mediation model predicting condom use at most recent sexual encounter with boyfriend or main sexual partner, with WMS as the independent variable, WMO as a covariate, and partner trust as the mediator. The total effect of WMS on condom use at most recent sexual encounter with boyfriend or main sexual partner was not significant; as such, there was not a relationship to be partially mediated by partner trust. Further, the indirect effect of WMS on condom use at most recent sexual encounter with boyfriend/main partner through partner trust was non-significant, as indicated by the 95% CI for the indirect effect. However, the relationship between WMS and partner trust was significant (p < .001); for every one standard deviation increase in WMS score, partner trust increased by 0.19 standard deviations.

Table 15, Model B displays results from the mediation model predicting condom use at most recent sexual encounter with boyfriend or main sexual partner, with WMO as the independent variable, WMS as a covariate, and partner trust as the mediator. The total effect of

WMO on condom use at most recent sexual encounter with boyfriend or main sexual partner was not significant; as such, there was not a relationship to be partially mediated by partner trust.

Further, the indirect effect of WMO on condom use at most recent sexual encounter with boyfriend/main partner through partner trust was non-significant, as indicated by the 95% CI for the indirect effect.

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Hypothesis 2.5: Alcohol use prior to sexual encounters (past 3 months), mediated by sex-related alcohol expectancies. Table 16, Model A displays results from the mediation model predicting likelihood of having at least one or more sexual encounters involving alcohol in the past 3 months, with WMS as the independent variable, WMO as a covariate, and sex-related alcohol expectancies as the mediator. The total effect of WMS on having at least one sexual encounter involving alcohol during the past 3 months was not significant; as such, there was not a relationship to be partially mediated by sex-related alcohol expectancies. However, the relationship between WMS and sex-related alcohol expectancies was significant (p < .001); for every one standard deviation increase in WMS score, sex-related alcohol expectancies decreased by 0.18 standard deviations. Furthermore, the relationship between sex-related alcohol expectancies and having one or more sexual encounters involving alcohol during the past 3 months was significant; for every 1 standard deviation increase in sex-related alcohol expectancies, the odds of having no sexual encounters that involved alcohol during the past 3 months decreased by 45%. The indirect effect of WMS on having any sexual encounters involving alcohol through sex-related alcohol expectancies was significant, as indicated by the

95% CI for the indirect effect.

Table 16, Model B displays results from the mediation model predicting likelihood of having at least one or more sexual encounters involving alcohol in the past 3 months, with WMO as the independent variable, WMS as a covariate, and sex-related alcohol expectancies as the mediator. The total effect of WMO on having at least one sexual encounter involving alcohol during the past 3 months was not significant; as such, there was not a relationship to be partially mediated by sex-related alcohol expectancies. Further, the indirect effect of WMO on having any sexual encounters involving alcohol through sex-related alcohol expectancies was non-

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significant, as indicated by the 95% CI for the indirect effect. As in Table 16, Model A, the relationship between sex-related alcohol expectancies and having one or more sexual encounters involving alcohol during the past 3 months was significant; for every 1 standard deviation increase in sex-related alcohol expectancies, the odds of having no sexual encounters that involved alcohol during the past 3 months decreased by 45%.

Aim 3 Confirmatory Factor Analysis and Structural Equation Modeling Analyses

Results of confirmatory factor analyses (CFA) examining the proposed measurement models for Attachment and TGP latent constructs are presented, followed by structural equation modeling (SEM) results on the relationship between Attachment and the TGP constructs (model building procedures).

Attachment latent variable measurement model. The proposed Attachment latent construct was indicated by WMS and WMO. As noted by Muthén and Muthén (2014), model estimates based on two indicators may be unstable and the model may be unidentified. Indeed, the model was unidentified when analyzed as proposed. Muthén and Muthén (2014) suggest fixing the factor variance to 1 in order to identify the model and estimate path coefficients for inficator variables. As such, the factor variance for Attachment was set to 1 in order to evaluate the proposed measurment model for Attachment. Figure 7 and Table 17 provide the factor loadings for the Attachment latent construct; because WMS and WMO are continuous variables, path loadings are standardized linear regession coefficients. As depicted in Figure 7, the standardized path coefficents for WMS and WMO were significant, suggesting that these indicators measured the underlying Attachment construct.

Affective attachment and social norms latent variable measurement model. See

Figure 8 and Table 18 for results of the CFA of the proposed measurement model for Affective

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attachment and social norms. Typical age of sexual partners and all the items from the Peer

Norms for Risky Behavior Scale were significantly associated with the underlying Affective attachment and social norms TGP construct. Note that all indicator variables were ordinal; as such, path loadings are standardized probit regression coefficients.

Sexual division of power latent variable measurement model. See Table 19 and

Figure 9 for results of the CFA of the proposed measurement model of the Sexual division of power latent variable. Coerced sex, physical abuse, and emotional abuse were significantly associated with the underlying Sexual division of power construct. Further, all of the items on the control subscale of the Power in Relationships Scale, as well as all of the items on the Fear of

Condom Negotiation Scale were significantly associated with the underlying Sexual division of power construct. All path loadings (for both ordinal and dichotomous indicators) are standardized probit regression coefficients.

Sexual HIV/STI risk behaviors latent variable measurement model. The measurement model for Sexual HIV/STI risk behaviors that was initially proposed included the following variables as indicators: (a) proportion condom use for vaginal sex, (b) number of life sexual partners, (c) number of sexual partners during past 3 months, (d) condom use at most recent sexual encounter involving alcohol, and (e) condom use at most recent sexual encounter not involving alcohol. Although it is possible that there was a degree of overlap between number of lifetime and past 3 month partners, the correlation between these variables (r = 0.58, p < .001) indicated that the magnitude of the association between them was moderate and that it was appropriate to include them as separate indicators in the model. Initial CFA analyses indicated that only lifetime number of sexual partners and number of recent sexual partners were associated with the underlying Sexual HIV/STI risk behaviors. Proportion condom use for

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vaginal sex (= 0.07, p = 0.08), condom use at most recent sexual encounter not involving alcohol (= .10, p = 0.09), and condom use at most recent sexual encounter involving alcohol

(= 0.16, p = 0.27) did not have significant factor loadings on the Sexual HIV/STI risk behaviors latent construct, and were removed from the model. Since the Sexual HIV/STI risk behaviors construct was the main outcome variable in the overall structural model including all TGP constructs, and being that estimates for constructs with less than three indicators may be unstable, it was decided to add another indicator to the model: having any STI at baseline.

Results of CFA analyses with this indicator (also including lifetime and recent number of sexual partner measures) suggested that having any STI at baseline was also significantly associated with the underlying Sexual HIV/STI risk behaviors latent construct. See Figure 10 and Table 20 for the measurement model and results of CFA analyses for the Sexual HIV/STI risk behaviors latent construct. Path loadings presented in Figure 10 are standardized logit coefficients (for both the dichotomous and the count variables).

Behavioral risk latent variable measurement model. See Table 21 and Figure 11 for the standardized path coefficient estimates for the measurement model of Behavioral risk. As proposed, all of the partner communication self-efficacy items, and the Sex Refusal Self-Efficacy

Scale items were associated with the underlying Behavioral risk construct. One item on the partner communication frequency subscale, “During the past 3 months, how many times have you and your boyfriend or sex partner(s) talked about his sexual history?” was not associated with the Behavioral risk latent factor (= -0.01, p = 0.74), and as such, was removed from the measurement model. All other partner communication frequency items were associated with the

Behavioral risk latent factor. Further, frequency of substance use during sexual encounters was

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associated with the underlying Behavioral risk construct. All path loadings presented in Table 21 and Figure 11 are standardized probit regression coefficients.

Affective personal risk (depression) latent variable measurement model. The proposed indicators for the Affective personal risk latent factor were the items of the revised

CESD-R. As depicted in Table 22 and Figure 12 all of these items were associated with the underlying Affective personal risks factor. Because this factor is indicated by items assessing depression in the current study, it may be conceptualized as a Depression symptoms factor. All path loadings presented in Table 22 and Figure 12 are standardized probit regression coefficients.

Knowledge based personal risk latent variable measurement model. The proposed indicators for the Knowledge based personal risk latent factor were the items on the HIV/STD

Knowledge Scale. As shown in Table 23 and Figure 13, all of these items were associated with the underlying Knowledge based personal risk latent factor. Note that because the current study operationalized this TGP factor as knowledge pertaining to HIV/STD, it should be interpreted as such, and not a general knowledge index. All path loadings presented in Table 23 and Figure 13 are standardized probit regression coefficients.

Model building procedures and assessment of structural model fit. The following presents results from the structural equation modeling results examining the association between

Attachment and the following TGP latent constructs: (a) Affective attachment and social norms;

(b) sexual division of power; (c) Sexual HIV/STI risk behaviors; (d) Behavioral risk; and (e)

Affective personal risk (depression symptoms).

Association between Attachment and Affective attachment and social norms. Results indicated that Attachment was associated with Affective attachment and social norms (=-0.65,

SE = 0.20, p = 0.001). This path loading is interpreted as a standardized linear regression

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coefficient; for each one standard deviation increase in Attachment, Affective attachment and social norms decreases by 0.65 standard deviations. In addition, the associations between

Attachment and its indicators (WMS and WMO) and Affective attachment and social norms and its corresponding indicators remained significant. Several model fit indices suggested goodness of model fit: CFI = 1.00; RMSEA = 0.0. Further, the chi-square difference test was significant

(2 = 331.69, df = 5 , p < .001), indicating the SEM model with Attachment explained the

Affective attachment and social norms outcome data better than the model that did not include

Attachment (i.e., the CFA for Affective attachment and social norms). Furthermore, the chi- square goodness of fit statistic for the model that included Attachment was non-significant

(2 = 3.36, df = 9 , p = 0.95), indicating good model fit. Age was not included as a covariate in this model because it was not associated with Affective attachment and social norms (p = 0.95).

Association between Attachment and Sexual division of power. Attachment was significantly associated with Sexual division of power (= 0.50, SE = 0.09, p < 0.001). For every one standard deviation increase in Attachment, sexual division of power increased by 0.5 standard deviations. Associations between Attachment and sexual division of power and their respective indicators remained significant in the SEM analyses. Model fit indices suggested goodness of model fit: CFI = 0.997; RMSEA = 0.02. Further, the chi-square difference test was significant (2 = 192.74, df = 5 , p < .001), indicating that the SEM model with Attachment explained the Sexual division of power outcome data better than the model that did not include

Attachment (i.e., the CFA for Sexual division of power). However, the chi-square goodness of fit statistic for the model that included Attachment indicated poor model fit (2 = 193.2, df = 159, p

< 0.05). Age was not included as a covariate in this model because it was not associated with

Sexual division of power (p = 0.96).

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Association between Attachment and Sexual HIV/STI risk behaviors. Attachment was associated with Sexual HIV/STI risk behaviors (= -0.19, SE = 0.05, p < 0.001). Associations between Attachment and Sexual HIV/STI risk behaviors and their respective indicators remained significant in the SEM analyses. Age was included as a covariate in this model because it was associated with Sexual HIV/STI risk behaviors (p < 0.001). The model fit indices available for this analysis, given the default estimator (maximum likelihood estimation with robust standard errors; MLR) for models with count data, included the AIC and BIC. Muthén and Muthén

(2014), suggest comparing the log-likelihood of nested models that include count data. The difference in log-likelihood is distributed as a chi-square statistic, and can be used in place of chi-square difference tests to determine if there is a significant difference between nested models

(UCLA Statistical Consulting Group). For these analyses, the CFA for Sexual HIV/STI risk behaviors was treated as the null model, and the SEM model that included Attachment was treated as the alternative model; the CFA model was nested within the SEM model. The estimate obtained (-130.44) did not exceed, the critical value (2 = 12.59, df = 6); as such the p-value was non-significant, indicating that the SEM model with Attachment did not account for variance in the Sexual HIV/STI risk behaviors outcome data beyond what was accounted for in the more parsimonious CFA model.

Association between Attachment and Behavioral risk. The model of the association between Attachment and behavioral risk would not converge due to a negative residual variance for WMS.

Association between Attachment and Affective personal risk (depression symptoms).

Attachment was not associated with Affective personal risk (p = 0.29). Further, the path coefficients between Attachment and WMS and WMO were non-significant in the structural

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model. Age was not included as a covariate in this model because it was not associated with

Affective personal risk (p = 0.86). However, model fit indices suggested good of model fit: CFI

= 1.00; RMSEA = 0.0. The chi-square test of model fit indicated good model fit (2 = 17.1, df =

22 , p = 0.76). Further, the chi-square difference test was significant (2 = 430.1, df = 27 , p <

.001), indicating the SEM model with Attachment explained the Affective personal risk outcome data better than the model that did not include Attachment (i.e., the CFA for Affective personal risk).

Assessment of elaborated models. Since the model of the association between

Attachment and Sexual HIVSTI Risk Behaviors did not account for variance in the Sexual

HIV/STI risk behavior outcome data beyond what was accounted for in the more parsimonious measurement model, and being that this association was the central and novel aspect of the current study, assessment of further elaborated models was not pursued. As such, Aim 3 analyses culminated in an examination of the association between Attachment and TGP constructs.

Discussion

African American women are disproportionately affected by HIV and other STI (CDC,

2014, 2015a). Being that the vast majority of HIV-infected women acquire HIV through unprotected heterosexual contact (CDC, 2015a), research investigating the psychosocial factors associated with sexual risk behaviors among African American women is a high public health priority. The aims of the current study were to examine the attachment-related constructs, working model-self (WMS) and working model-other (WMO) in relation to sexual risk among young adult African American women.

Overall, the hypothesized associations between WMS and WMO and risky sexual behavior were not supported. Further, the associations between WMS and number of sexual

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partners, as well as the associations between WMS and risk factors for risky sexual behavior engagement should be interpreted within the context of the number of analyses performed, which may have inflated Type I error rate. As such, findings indicated that the attachment-related constructs WMS and WMO were not associated with risky sexual behavior engagement among the current sample of African American women.

WMS and WMO did not predict engagement in various condom use behaviors (e.g., proportion condom use for vaginal sex, condom use at recent sexual encounters), which are arguably the most robust indicators of HIV-risk. Notably, a high percentage (84.46%) of the current sample reported having a boyfriend or main partner, and 75.76% of those participants did not use a condom at their most recent sexual encounter. These findings are consistent with previous research suggesting low overall levels of condom use among African American women with steady sexual partners (Crosby et al., 2000; Kanu et al., 2009). Non-condom use norms within steady relationships may symbolize trust and commitment (Conley & Rabinowitz, 2004;

East, Jackson, O'Brien, & Peters, 2007; Macaluso, Demand, Artz, & Hook III, 2000; Wildsmith,

Manlove, & Steward-Streng, 2015), and heterosexual couples may prefer contraceptive methods that prevent pregnancy, but do not afford protection from HIV/STI (e.g., oral contraceptives, intrauterine devices; Bolton, McKay, & Schneider, 2010; Wildsmith et al., 2015). It is possible that insufficient variation in condom use may have interfered with detecting any associations between WMS, WMO and condom use; however, given the lack of support for associations between WMS and WMO and various other sexual risk behaviors, it is also possible that attachment is merely not associated with condom use behaviors.

Further, although there was an indirect effect of WMS on any condom use for anal sex through partner communication self-efficacy (lower WMS associated with lower partner

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communication self-efficacy, which in turn was associated with a lower likelihood of condom use for any anal sex occasions), the fact that only one indirect effect was detected may be an artifact of inflated Type I error rate. In addition, direction of causality cannot be inferred based on these findings due to the cross-sectional nature of the data. Indeed, it is possible that negative partner communication experiences could reduce partner communication self-efficacy and lead to more negative WMS, as opposed to negative WMS directly causing lower partner communication self-efficacy.

While WMS and WMO were not associated with condom use behaviors, lower WMS was associated with higher numbers of sexual partners (both lifetime and past 3 months) whereas

WMO was not. This finding is consistent with previous research suggesting that negative WMS may underlie the association between fearful attachment (i.e., negative WMS and negative

WMO) and a greater number of sexual partners among women (Ciesla et al., 2004; Olley, 2010).

Females with more negative WMS may pursue sexual encounters to affirm their perceived lovability, or they may be less likely to refuse males’ sexual advances because they seek approval from partners to validate their self-worth (Bartholomew & Horowitz, 1991). These findings are also consistent with other research indicating that WMS may be associated with sexual behaviors among women, whereas WMO may be associated with sexual behavior among men. For example, Schmitt and Jonason (2014) found that lower WMS scores were associated with short-term mating strategies (i.e., preference for casual sex) among women, but not men; whereas lower WMO scores were associated with a preference for casual sex among men, but not among women. Although WMS was associated with number of recent sexual partners, other factors such as partner type (Kanu et al., 2009) and length of relationship (Corneille, Zyzniewski,

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& Belgrave, 2008; Noar et al., 2012) may have influenced whether females in the current sample used condoms with these partners.

Though WMS was associated with number of sexual partners during the past 3 months, neither WMS nor WMO was associated with having any recent casual sexual partners during the same time period. While these findings are somewhat inconsistent with each other, research indicates that heterosexual young adult African Americans distinguish between three types of partners: (1) one-night-stands, (2) “regular” casual partners, and (3) main partners, and that these partner types may differentially affect sexual behaviors and perceived risk associated with unprotected sex (Noar et al., 2012). It is possible that some participants may have interpreted the term “casual sexual partner” as referring to a one-night-stand, whereas others interpreted the term as a regular casual partner, which may have resulted in the inconsistent findings in the association between WMS and number of sexual partners in the past 3 months and WMS and having any casual sexual partners in past 3 months. Further, while findings from previous research indicate that insecure attachment is associated with having casual sexual partners among females (Ahrens et al., 2012; M. L. Cooper, Shaver, et al., 1998; Paul et al., 2000), differences between previous research and the current study in the operational definition of casual sex (e.g., sex with a stranger, sex without knowing partner’s history), the time frame assessed (lifetime casual sex), and the demographic characteristics of the samples (e.g., adolescents, predominantly White college students) may partially account for inconsistent findings. For example, Ahrens et al. (2012), Cooper, Shaver et al. (1998), and Paul et al. (2000) assessed ever having some form of casual sex, whereas the current study assessed having any casual sexual partners in the past 3 months. It is possible that determining the association

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between having casual sexual partners and WMS and WMO may require assessing behaviors within a longer timeframe (e.g., 6 months, lifetime).

Because there tends to be a consistent association between alcohol use prior to sexual encounters and greater likelihood of unprotected sex (Brown & Vanable, 2007; Kiene et al.,

2009), alcohol use prior to sexual encounters was included as an outcome. WMS and WMO were also not associated with frequency of consuming alcohol prior to sexual encounters during the past 3 months, or with consuming alcohol prior to any sexual encounters during the past 3 months. However, WMS had an indirect effect on consuming alcohol prior to any sexual encounters through sex-related alcohol expectancies. Based on these findings, African American females in the current sample with lower WMS scores had greater sex-related alcohol expectancies, and having greater sex-related alcohol expectancies was associated with increased odds of having consumed alcohol prior to any sexual encounters.

Previous research indicates that some African American women may consume alcohol prior to sexual encounters because they believe alcohol increases sexual confidence (Hutton et al., 2015). It is possible that low sexual confidence is a facet of negative WMS, and that the association between negative WMS and greater sex-related alcohol expectancies may be driven by the belief that alcohol decreases these negative thoughts within sexual encounters.

Furthermore, African American women may view alcohol as reducing the amount of embarrassment or inhibition they experience within sexual encounters (Hutton et al., 2015;

Brown, Talley, Littlefield, & Gause, 2016). Being that women with lower WMS seek external validation through acceptance from others, they may be more inclined to want to reduce the amount of embarrassment or inhibition they experience so that they are more likely to fulfill partners’ requests and gain their approval.

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Although partial mediation hypotheses (Aim 2) were not supported due to neither WMS nor WMO having direct effects on sexual risk outcomes, lower WMS was associated with all the proposed mediators (e.g., lower partner communication self-efficacy, lower levels of partner trust), whereas WMO was not associated with any of the mediators. These findings indicate that while WMS may not be directly associated with risky sexual behaviors, it is directly associated with various risk factors for engagement in risky sexual behaviors. Being that WMS is the product of previous interpersonal experiences, it is possible that other factors not accounted for in these analyses may be associated with both lower WMS and these risk factors for risky sexual behavior, such as prior experiences of emotional, physical, or sexual abuse.

The third aim of the present study was to evaluate the addition of attachment within the framework of the Theory of Gender and Power (TGP; Wingood & DiClemente, 2000). Overall, less secure attachment was associated with increased exposure to the social acquired risks that occur within the Structure of Affective Attachment and Social Norms and increased exposure to the physical acquired risks that occur within the Structure of Sexual Division of Power.

Attachment was considered a risk factor in the current model since it reflects personal beliefs and attitudes regarding interpersonal relationships. Results indicate that attachment may represent a risk factor that affects acquired risks (i.e., exposures), and may provide a means for identifying women at elevated risk for being exposed to (or who have already been exposed to) various sociocultural factors that increase probability of contracting HIV/STI. However, it should be noted that although attachment bonds begin to form during childhood and continue to develop throughout adulthood, it remains unclear whether attachment causes increased social and physical exposures (i.e., acquired risks) or vice versa. Indeed, it is entirely possible that social

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and physical exposures may affect attachment being that attachment is the product of social interactions and experiences with others.

Limitations

Several limitations of the current study should be noted. Attachment was assessed by having participants rate the degree to which a brief description of each attachment style was characteristic of them; assessing each attachment style with this single item may have measured a narrow range of the attachment-related beliefs and behaviors characteristics of each style. A measure with multiple items to assess each attachment style or an inventory that directly assesses each attachment dimension using multiple items may have provided a more comprehensive and accurate assessment of attachment (Shaver & Fraley, 2004). Being that there are currently no measures that directly assess WMS and WMO using multiple, unique items for each dimension, future research may consider a different conceptualization of attachment for which such a measure exists, such as the Experiences in Close Relationships Scale-Revised (ECR-R) and the corresponding avoidance and anxiety dimensions.

Additionally, since self-ratings of attachment may be limited by degree of personal insight, future studies should consider including objective measures to complement self-report questionnaires and reduce mono-method bias. Attachment interviews such as the Adult

Attachment Interview (AAI; C. George et al., 2000) and the Current Relationship Interview

(CRI; J. A. Crowell & Owens, 1998) consider objective information (e.g., discourse style, coherence of narrative) in addition to an individual’s attachment-related experiences to determine attachment style (J. A. Crowell & Treboux, 1995).

Regarding the measurement of WMS and WMO specifically, Griffin and Bartholomew

(1994) noted that WMS “was obtained by summing the ratings of the two attachment patterns

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with positive self models (secure+dismissing) and subtracting the ratings of the two patterns with negative self models (preoccupied+fearful)” (p. 432); and WMO “was obtained by summing the ratings of the two patterns with positive other models (secure+preoccupied) and subtracting the ratings of the two patterns with negative other models (dismissing+fearful)” (p. 432). Although these formulas conceptually align with the four-category model of attachment, a sound theoretical rationale for using addition and subtraction of attachment style ratings to calculate

WMS and WMO is lacking, as is an empirical rationale for these derived scores. Indirectly assessing WMS and WMO by adding and subtracting self-report ratings of brief single-item attachment style descriptions may have provided an inaccurate assessment of WMS and WMO, which may have implications for results across study aims.

Beyond the measurement issues noted above, there were some additional methodological issues pertaining to Aim 3. Indicator variables were measured on various scales (e.g., ordinal, count, binary, continuous), which may make stable estimation of covariance matrices more difficult. Additionally, the measurement model for attachment had only two indicators (WMS and WMO), and structural models with constructs that have only two indicators are more prone to problems with model fit and path estimates (Kline, 2011). Since attachment was the central aspect of the proposed structural model, the potential impact of this two-indicator construct on the validity of the results must be acknowledged. Furthermore, the magnitude of the correlation between WMS and WMO (r = .14, p < .01) brings into question the statistical appropriateness of modeling these variables as indicators of the same latent construct (Kline, 2011). The lack of a moderate association (r >.50; Kline, 2011) between WMS and WMO is consistent with Griffin and Bartholomew’s (1994) findings and conceptually aligns with the four-category model of attachment. Although they are dimensions of attachment, WMS and WMO may be more

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accurately conceptualized as distinct attachment-related constructs, and it may have been more suitable to separate the attachment construct into multiple factors: one WMS latent factor and one WMO latent factor.

Overall, a more general limitation of the current study included the inflated Type I error rate due to the number of hypotheses tested, which was not statistically accounted for in the analyses. To address this issue, a more conservative alpha could have been utilized based on the number of hypotheses tested (e.g., Bonferroni correction; Howell, 2013). For example, 12 hypotheses were tested in Aim 1a, with alpha set to 0.05. Controlling for Type I error using a

Bonferroni correction would have reduced alpha to 0.004; however, this correction would not have altered conclusions drawn from Aim 1a results being that associations were significant at the p < .001 level.

Additional limitations of the current research include the nature of the sample, as well as implications for generalizability. The sample consisted exclusively of young heterosexual

African American women who reported consuming alcohol on at least 3 occasions during the past 90 days. As such, results may not generalize to males, women of other racial/ethnic backgrounds, or women who do not consume alcohol. Moreover, the majority of the sample was currently in a relationship, which may have reduced variability in sexual risk behaviors and limits the generalizability of the current study’s findings to samples of African American women not in relationships.

Future Directions

Although results of the current study indicated that WMS and WMO were not associated with risky sexual behavior engagement among African American women, future studies should nonetheless address several of the methodological limitations noted above, such as inclusion

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criteria for sample selection and the measurement and operationalization of attachment.

Research examining the association between attachment and risky sexual behavior would benefit from greater variability in the relationship status of the sample, which in turn may provide more variability in sexual behaviors such as condom use. Future studies should ensure that samples are more diverse with regards to relationships status. Further, attachment measurement issues should be addressed by using a multi-item self-report measure that directly assesses attachment dimensions (e.g., ECR-R), or complementing that questionnaire with an attachment interview

(e.g., AAI). Regarding the limitation of the calculation of WMS and WMO scores, post hoc analyses were conducted to explore an alternative method for utilizing the Relationship

Questionnaire. This involved examining a series of simple zero-inflated negative binomial regression models with attachment style ratings (e.g., rating for the fearful description) predicting proportion condom use for vaginal sex. Results indicated that neither the fearful, dismissive, preoccupied, nor secure item was associated with proportion condom use for vaginal sex. As such, future research may consider utilizing a different conceptualization of attachment

(e.g., anxiety and avoidance). This line of research may also benefit from the development of a psychometrically valid and reliable multi-item measure that directly assesses WMS and WMO.

Regarding methodological issues related to Aim 3, future research should consider conceptualizing WMS and WMO as separate latent constructs utilizing an approach similar to

Griffin and Bartholomew’s (1994), with indicators of WMS and WMO being self-report (as measured in the current study), friend report, and peer interview sources. Alternatively, the ECR-

R may be utilized to create an attachment anxiety and an attachment avoidance latent construct being that the ECR-R assesses anxiety and avoidance using 18 items each, which may be treated

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as indicators for each construct. Future SEM studies of the TGP should ensure that variables are measured on the same scale to facilitate covariance matrix estimation.

Additionally, that condom use indicators did not load onto the Sexual HIV/STI risk behavior latent construct (Aim 3 CFA analyses) while having any STI at baseline (a proxy for unprotected sex) did suggests that although condom use was low across individuals and situations in the current sample, actual HIV/STI-risk associated with unprotected sex (as indicated by STI infection) remains a considerable public health concern. As such, future research should consider examining the association between WMS and WMO and objective measures of HIV/STI-risk.

Future research could also investigate potential mediators (e.g., sex refusal self-efficacy, motivations for engaging in sexual encounters) of the association between lower WMS and higher numbers of lifetime and recent sexual partners. Similarly, in order to better understand the relationship between WMS and the alcohol-risky sex relation, future research should focus on the particular sex-related alcohol expectancies associated with lower WMS among African

American women.

Longitudinal studies would also expand upon the current study’s findings. In particular, longitudinal assessment of the association between attachment anxiety and avoidance and HIV prevention intervention targets (i.e., mediators in Aim 2 analyses) could examine whether these constructs affect response to HIV prevention interventions. For example, a future study could investigate whether interventions that target (a) partner communication self-efficacy, (b) fear of condom negotiation, (c) peer-norms for risky sexual behaviors, (d) partner trust, and/or (e) sex- related alcohol expectancies are differentially efficacious in altering these constructs and reducing risky sexual behaviors depending on anxiety and avoidance levels at baseline.

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Conclusions

African American women represent a population that is disproportionately affected by

HIV/STI. Behavioral interventions to reduce HIV/STI prevalence among African American women tend to target psychosocial factors associated with engagement in risky sexual behaviors.

The current study proposed that the attachment-related constructs WMS and WMO might represent psychosocial constructs associated with risky sexual behavior among African

American women. Overall, results indicated that WMS and WMO were not associated with various condom use behaviors, having casual sexual partners, or indices of alcohol use prior to sexual encounters. WMS was associated with number of lifetime and recent sexual partners; however, given the number of analyses performed and the overall null findings, these results do not suggest that assessing or addressing WMS would augment HIV prevention efforts tailored to

African American women. WMS was associated with psychosocial factors associated with engagement in risky sexual behaviors (e.g., partner communication self-efficacy, fear of condom negotiation), but not directly associated with risky sexual behaviors. Overall, results indicated that WMS and WMO do not represent psychosocial factors associated with risky sexual behavior and future studies examining the association between attachment and sexual risk engagement would benefit from alternate operationalization of the attachment construct.

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Self

Good

Dismissive Secure Self-worthiness Self-worthiness; lovability Dismissing of intimacy Others are accepting and responsive Counter-dependent Comfortable with intimacy and autonomy

Other Other

Bad Good

Fearful Preoccupied Unworthiness; unlovability Fearful of intimacy; socially Unworthiness; unlovability Preoccupied with relationships avoidant Others are untrustworthy and Strive for self-acceptance by gaining rejecting acceptance of others

Bad

Self

Good

Figure 1. Bartholomew and Horowitz’s (1991) four-category model of attachment.

145

Figure 2. Conceptual model from DePadilla et al. (2011) for the Theory of Gender and Power as applied to condom use among women.

146

147

148

Table 1

Demographic Characteristics of Participants (N =560)

Demographic Variables N (%) Possible Reported Range Range Age M (SD) 20.58 (1.89) 18-24 17-24

Number of forms of government aid M (SD) 1.35 (0.93) 0-4 0-4

Currently employed 152 (27.14) -- --

Highest level of education

8th grade or less 5 (0.89) -- --

Some high school 180 (32.14) -- --

Graduated high school or GED 231 (41.25) -- --

Some college 128 (22.86) -- --

Graduated college 11 (1.96) -- --

Current living situation

Alone 87 (15.54) -- --

With a roommate 84 (15.00) -- --

With a boyfriend 98 (17.50) -- --

With parents 271 (48.39) -- --

Has a boyfriend or main partner 473 (84.46) -- --

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Table 2

Working Model-Self (WMS) and Working Model-Other (WMO) Scores by Dominant

Attachment Style (N=560)

Dominant attachment stylea N (%) WMS WMO M (SD) M (SD) Secure 177 (31.61) 2.49 (1.27) 1.70 (1.27)

Fearful 279 (49.82) -0.35 (1.64) -0.92 (1.64)

Dismissive 51 (9.11) 2.80 (1.46) -1.47 (1.50)

Preoccupied 38 (6.79) -1.42 (1.20) 2.74 (1.55)

Preoccupied & Dismissive both rated as dominant 15 (2.68) 0.80 (1.21) 0.80 (1.21) a Dominant attachment style was determined by the item with the highest rating on the Relationship Questionnaire. Fearful takes precedence over other styles in the event of a tie; Dismissing and Preoccupied take precedence over Secure in the event of a tie (Ciesla et al., 2004). There are no guidelines for instances in which Preoccupied and Dismissive are tied for the highest rating, therefore these results (N=15) are presented as such.

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Table 3

Sexual Risk Behaviors Among Participants (N=560)

Sexual Risk Variables N (%) Possible Reported Range Range Proportion condom use for anal sex .21 (0.37) 0-1 0-1 (past 3 months)a M (SD)

Proportion condom use for vaginal sex .33 (0.31) 0-1 0-.96 (past 3 months)b M (SD)

Number of sexual partners (lifetime) 12.83 (18.48) 1-999 1-200 M (SD)

Number of sexual partners (past 3 months) 2.36 (3.03) 1-99 1-34 M (SD)

Had casual sex partner(s) (past 3 months) 247 (44.11) -- --

Frequency of drinking alcohol before having sexual intercourse (past 3 months) Never 148 (26.43) -- --

Almost never 96 (17.14) -- --

Sometimes 224 (40.00) -- --

Almost always 62 (11.07) -- --

Always 30 (5.36) -- --

Did not use a condom at most recent sexual 271 (65.78) -- -- encounter involving alcoholc

Casual partner at most recent sexual encounter 136 (33.01) -- -- involving alcoholb

Did not use a condom at most recent sexual 360 (64.29) -- -- encounter not involving alcohol

Casual partner at most recent sexual encounter not 169 (30.18) -- -- involving alcohol

Never used a condom for anal sex (past 3 months)a 69 (68.32) -- --

Did not use condom with boyfriend/main partner are 350 (75.76) -- -- most recent sexual encounterd a N=101 for women reporting anal sex in the past 3 months. b N= 548. c N=412 for women reporting they have ever consumed alcohol before having sex. d N=462; only asked of women who reported that they currently have a boyfriend or main partner.

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Table 4

Theory of Gender and Power (TGP) Construct Characteristics Among Participants (N=560)

TGP Variables N (%) Possible Reported Range Range Partner trusta M (SD) 22.57(6.22) 5-35 5-35

Fear of condom negotiation M (SD) 9.02 (4.39) 7-40 7-35

Partner communication self-efficacy M (SD) 19.32(4.23) 6-28 6-28

Peer norms for risky sex M (SD) 9.79 (4.06) 5-25 5-25

Alcohol outcome expectancies M (SD) 17.84(8.01) 8-36 8-36

Relationship power M (SD) 28.74(5.71) 9-36 9-36

HIV/STD knowledge M (SD) 7.62 (2.46) 0-11 0-11

Depression M (SD) 13.36(5.85) 8-32 8-32

Partner communication frequency M (SD) 9.59 (3.92) 5-20 5-20

Sex refusal self-efficacy M (SD) 23.64(4.54) 7-28 7-28

Ever been forced to have vaginal sex 118 (21.07) -- --

Ever been physically abused 178 (31.79) -- --

Ever been emotionally abused 241 (43.04) -- --

Typical age of sex partners

Much younger (4 or more years younger) 5 (0.89) -- --

Younger (2-3 years younger) 11 (1.96) -- --

About the same age 217 (38.75) -- --

Older (2-3 years older) 202 (36.07) -- --

Much older (4 or more years older) 125 (22.32) -- -- a N=473 for women reporting currently being in a relationship.

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153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

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Table 17

Results of CFA of proposed measurement model for Attachment

Indicator Variable β Standard Error

Working model-self (WMS) 0.48*** 0.09

Working model-other (WMO) 0.29** 0.01 Note. Path loadings are standardized linear regression coefficients. ** p < .01, *** p < .001.

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Table 18

Results of CFA of proposed measurement model for Affective Attachment and Social Norms

Indicator Variable β Standard Error

In general, how old are the people you have sex with? 0.16* 0.02

Peer Norms for Risky Sexual Behavior items

PN1: How many of your friends think that: It's okay 0.66*** 0.04 to have vaginal or anal sex without a condom?

PN2: How many of your friends think that: It's okay 0.73*** 0.04 to have sex with someone you just met?

PN3: How many of your friends think that: Cheating 0.79*** 0.04 on your partner is okay?

PN4: How many of your friends think that: It's safe to 0.70*** 0.04 have sex when you are high on drugs or alcohol?

PN5: How many of your friends think that: You don't 0.71*** 0.04 have to use a condom with someone you know well? Note. Path loadings are standardized probit regression coefficients. *p < .05, *** p < .001.

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Table 19

Results of CFA of proposed measurement model for Sexual Division of Power

Indicator Variable β Standard Error Has anyone ever forced you to have vaginal sex when you -0.40*** 0.05 didn't want to?

Have you ever been physically abused? (hit, punched, -0.49*** 0.04 kicked, slapped, etc.)

Have you ever been emotionally abused? (threatened, -0.50*** 0.04 called names, etc.)

Fear of Condom Negotiation Items

FCN1: I have been worried that if I talked about using -0.54*** 0.03 condoms with my boyfriend or sex partner he would ignore my request.

FCN2: I have been worried that if I talked about using -0.88*** 0.02 condoms with my boyfriend or sex partner he would threaten to hit me.

FCN3: I have been worried that if I talked about using -0.92*** 0.01 condoms with my boyfriend or sex partner he would threaten to leave me.

FCN4: I have been worried that if I talked about using -0.90*** 0.02 condoms with my boyfriend or sex partner he would swear at me, or call me ugly names.

FCN5: I have been worried that if I talked about using -0.94*** 0.02 condoms with my boyfriend or sex partner he would hit, push or kick me.

FCN6: I have been worried that if I talked about using -0.92*** 0.01 condoms with my boyfriend or sex partner he would leave me.

FCN7: I have been worried that if I talked about using -0.81*** 0.02 condoms with my boyfriend or sex partner he would go out with other girls.

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Power in Relationships, Control subscale items

PRC1: If I asked my partner to use a condom, he would 0.59*** 0.03 get violent.

PRC2: Most of the time we do what my partner wants 0.69*** 0.02 to do.

PRC3: My partner won't let me wear certain clothes. 0.61*** 0.03

PRC4: When my partner and I are together I am pretty 0.69*** 0.03 quiet.

PRC5: I feel trapped or stuck in my relationship. 0.79*** 0.02

PRC6: My partner does what he wants even if I don't 0.75*** 0.02 want him to.

PRC7: I am more committed to our relationship than 0.69*** 0.03 my partner

PRC8: My partner always wants to know where I am. 0.53*** 0.03

PRC9: My partner gets more out of the relationship 0.67*** 0.03 than I do. Note. Path loadings are standardized probit regression coefficients. *** p < .001.

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Figure 9. Sexual Division of Power latent construct measurement model. Note. FCN1-FCN7 correspond with items on the Fear of Condom Negotiation scale; PRC1-PRC9 correspond with Control subscale items on the Power in Relationships scale (see Table 18). Path loadings are standardized probit regression coefficients. *** p < .001.

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Table 20

Results of CFA of proposed measurement model for Sexual HIV/STI Risk Behaviors

Indicator Variable β Standard Error

Number of lifetime sexual partners 0.83*** 0.04

Number of sexual partners (past 3 months) 0.52*** 0.03

Any STI at baseline 0.25* 0.10 Note. Path loadings are standardized linear regression coefficients. * p < .05, *** p < .001.

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Table 21

Results of CFA of proposed measurement model for Behavioral Risk

Indicator Variable β Standard Error

In the past 3 months, how much of the time did you drink 0.16*** 0.04 alcohol before you had sexual intercourse

Partner Communication Self-Efficacy Items

PCSE1: How hard is it for you to ask how many sex -0.60*** 0.03 partners he has had?

PCSE2: How hard is it for you to ask if he is having sex -0.66*** 0.02 with you and other women?

PCSE3: How hard is it for you to ask if he has an STD? -0.56*** 0.03

PCSE4: How hard is it for you to ask if he would use a -0.81*** 0.01 condom?

PCSE5: How hard is it for you to demand that he use a -0.85*** 0.01 condom? PCSE6: How hard is it for you to refuse to have sex if -0.69*** 0.02 he won't wear a condom?

Partner Communication Frequency Items

PCF1: During the past 3 months, how many times have 0.13*** 0.03 you and your boyfriend or sex partner(s) talked about how to prevent pregnancy?

PCF2: During the past 3 months, how many times have 0.34*** 0.03 you and your boyfriend or sex partner(s) talked about how to use condoms?

PCF3: During the past 3 months, how many times have 0.92*** 0.01 you and your boyfriend or sex partner(s) talked about how to prevent getting HIV (the virus that causes AIDS)?

PCF4:During the past 3 months, how many times have 0.92*** 0.01 you and your boyfriend or sex partner(s) talked about how to prevent getting STDs?

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Sex Refusal Self-Efficacy Scale Items

SRSE1: How sure are you that you would be able to -0.66*** 0.03 say NO to having sex with someone you have known for a few days or less?

SRSE2: How sure are you that you would be able to -0.79*** 0.02 say NO to having sex with someone you want to date again?

SRSE3: How sure are you that you would be able to -0.75*** 0.02 say NO to having sex with someone who you want to fall in love with you?

SRSE4: How sure are you that you would be able to -0.74*** 0.02 say NO to having sex with someone who is pressuring you to have sex?

SRSE5: How sure are you that you would be able to -0.70*** 0.02 say NO to having sex with someone after you have been drinking alcohol?

SRSE6: How sure are you that you would be able to -0.77*** 0.02 say NO to having sex with someone who refuses to wear a condom?

SRSE7: How sure are you that you would be able to -0.68*** 0.02 say NO to having sex with someone who you have had sex with before? Note. Path loadings are standardized probit regression coefficients. *** p < .001.

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Figure 11. Behavioral Risk latent construct measurement model. Note. PCSE1-PCSE6 correspond with items on the Partner Communication Self-Efficacy subscale items on the Partner Communication History scale; PCF1-PCF4 correspond with items on the Partner Communication Frequency subscale items on the Partner Communication History scale; SRSE1-SRSE7 correspond with the items on the Sex Refusal Self- Efficacy scale (see Table 19). Path loadings are standardized probit regression coefficients. *** p < .001.

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Table 22

Results of CFA of proposed measurement model for Affective Personal Risk

Indicator Variable β Standard Error

During the past week I have…

D1: I felt that I could not shake off the blues even with 0.73*** 0.03 help from my family and friends.

D2: I felt depressed. 0.89*** 0.01

D3: I thought my life had been a failure. 0.86*** 0.02

D4: I felt fearful. 0.80*** 0.03

D5: My sleep was restless. 0.73*** 0.03

D6: I felt lonely. 0.86*** 0.02 D7: I had crying spells. 0.89*** 0.01

D8: I felt sad. 0.89*** 0.01 Note. Participants responded to items on a Likert scale: 1; Less than 1 day, 2; 1-2days, 2; 3-4 days, 4; 5-7 days. Path loadings are standardized probit regression coefficients. *** p < .001.

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Table 23

Results of CFA of proposed measurement model for Knowledge Based Personal Risk

Indicator Variable β Standard Error

KNW1: Birth control pills protect women against the AIDS 0.84*** 0.05 virus.

KNW2: Most people who have AIDS look sick. 0.67*** 0.05 KNW3: Men are more susceptible (or likely) to get an STD 0.57*** 0.05 infection than women.

KNW4: Having an STD can increase the risk of getting HIV. 0.25*** 0.06

KNW5: If a man has an STD, he will have noticeable 0.73*** 0.04 symptoms.

KNW6: STDs can cause infertility, spontaneous abortions 0.49*** 0.06 and still births.

KNW7: STDs can only be passed through open sores or 0.70*** 0.04 lesions. KNW8: If a man pulls out before orgasm (cumming), 0.84*** 0.05 condoms don't need to be used to protect against HIV.

KNW9: Vaseline and other oils should be used to lubricate 0.62*** 0.05 condoms.

KNW10: Condoms cause men physical pain. 0.67*** 0.05

KNW11: Most people who carry the AIDS virus look 0.25*** 0.06 healthy. Note. Participants responded to items by selecting either 1- True, 2- False, or 3- I don’t know. Path loadings are standardized probit regression coefficients. *** p < .001.

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