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Common Therapeutic Factors in Psychotherapy and Complementary and Alternative Medicine Treatments by Alexander Milovanov a Thes

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Common Therapeutic Factors in Psychotherapy and Complementary and Alternative Medicine Treatments by Alexander Milovanov a Thes Common Therapeutic Factors in Psychotherapy and Complementary and Alternative Medicine Treatments by Alexander Milovanov A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Arts in Psychology Waterloo, Ontario, Canada, 2017 © Alexander Milovanov 2017 Author’s Declaration I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Dozens of common therapeutic factors have been identified in psychotherapy research, but less attention has been devoted to integrating those factors into coherent theoretical frameworks. Frank and Frank (1991) proposed a comprehensive model with four therapeutic factors common across psychotherapies and other socially-sanctioned healing practices: emotionally charged and confiding relationships with healers, healing settings, persuasive treatment rationales, and specific procedures that engage clients in treatments. They held these factors as powerfully therapeutic because they mobilize hope in otherwise overwhelmed individuals. The current study used multi-group SEM to test Frank and Frank’s model across diverse treatment groups. Rogerian core conditions (empathy, care, genuineness), perceived practitioner credibility, quality of the healing setting, persuasiveness of treatment rationale, and client’s self-assessed hopefulness were measured in five groups of participants. The groups consisted of people receiving treatment for psychological issues from psychotherapists (n = 686) and various complementary and alternative medicine practitioners (n = 155), and those receiving treatment for pain-related issues from chiropractors (n = 518), massage therapists (n = 234), and acupuncturists (n = 100). Results from the cross-sectional model supported Frank and Frank’s hypothesis that their factors independently contribute to the prediction of outcomes across a broad range of healing practices and that their effects are partially mediated by hope. Results from longitudinal analyses based on psychotherapy (n = 138) and chiropractic (n = 134) groups (outcomes measured six to eight months later) showed partial support for Frank and Frank’s model. iii Acknowledgements I am very grateful to my supervisor, Dr. Jonathan Oakman, for his encouragement, inspiration, and guidance, in large things and small. I am also thankful to Dr. Walter Mittelstaedt for his thoughtful comments on my thesis and for the many ways in which he contributes to the Psychological Intervention Research Team (PIRT). This project has come to fruition not in small part due to the help of PIRT research assistants who spent countless hours bringing order out of the chaos of raw data. I am also grateful to Dr. David Moscovitch, who has taken the role of a reader for this thesis and has provided me with many excellent insights and constructive suggestions. Julia McNeil’s advice on methodology and statistical analyses has been tremendously helpful. I would also like to acknowledge the Canadian public, without whose contributions in the form of Social Sciences and Humanities Research Council of Canada funding my research would not have been possible. Last but not least, I am eternally grateful to my partner, Jasmine Pauk, my brother, Paul Milovanov, my family, my friends, and my cohort, for not only putting up with me but actually giving me the love and support that no graduate student can survive without. iv Table of Contents List of Tables . vi List of Figures. vii Introduction . 1 Method . 32 Results . 41 Discussion . 51 References . 65 Tables. 88 Figures. 100 Appendix A: Factor Analysis Results in the Construction of Measurement Models . 106 Appendix B: Factor Loadings for Measurement Models . 109 Appendix C: Example of Cross-sectional Structural Model With Estimates. 110 v List of Tables Table 1. Demographic Differences on Continuous Variables Between Treatment Groups. 88 Table 2. Demographic Differences on Categorical Variables Between Treatment Groups. 89 Table 3. Practitioner Types and Primary Health Issues for PsyPsych and PsyCAM treatment groups. 90 Table 4. Primary Health Issues for PainChir, PainMass, and PainAcu treatment groups. 91 Table 5. Demographic Information for Treatment Groups Used in the Longitudinal Model Compared to the Full Samples. 92 Table 6. Percent of Variance Explained in Each Common Factor by the Other Three Predictor Factors (R2 Values). 93 Table 7. Correlations Between Exogenous Factors in the PsyPsych Group. 93 Table 8. Correlations Between Exogenous Factors in the PsyCAM Group. 94 Table 9. Correlations Between Exogenous Factors in the PainChir Group. 94 Table 10. Correlations Between Exogenous Factors in the PainMass Group. 95 Table 11. Correlations Between Exogenous Factors in the PainAcu Group. 95 Table 12. Standardized Path Coefficients with 95% Confidence Intervals for the Cross- sectional Multi-group SEM Model. 96 Table 13. Standardized Coefficients with 95% Confidence Intervals for the Direct, Indirect, and Total Effects in the Cross-sectional Multi-group SEM Model. 97 Table 14. Standardized Path Coefficients with 95% Confidence Intervals for the Longitudinal Multi-group SEM Model. 98 Table 15. Standardized Coefficients with 95% Confidence Intervals for the Direct, Indirect, vi and Total Effects in the Longitudinal Multi-group SEM Model. 99 Table A1. ESEM Results for Treatment Rationale Items Using ML Estimation and Geomin Oblique Rotation. 106 Table A2. Correlations Between Five Geomin-rotated Treatment Rationale Factors. 106 Table A3. ESEM Results for Hope Items Using ML Estimation and Geomin Oblique Rotation. 107 Table A4. Correlations Between Five Geomin-rotated Hope Factors. 107 Table A5. ESEM Results for Positively Worded Hope Items Using ML Estimation and Geomin Oblique Rotation. 108 Table A6. Correlations Between Four Geomin-rotated Hope Factors. 108 Table B1. Factor Loadings for Measurement Models. 109 vii List of Figures Figure 1. Flow of participants through each stage of the study. 100 Figure 2. Structural model for the cross-sectional analysis. 101 Figure 3. Structural model for the longitudinal analysis. 102 Figure 4. Example of typical response distributions for positively worded and reverse scored items. 103 Figure 5. Multi-group SEM diagram with measurement models for the Rogerian factor predicted by credibility, healing setting, and treatment rationale. 104 Figure 6. Multi-group SEM diagram with measurement models for the treatment rationale predicted by the Rogerian factor, credibility, and healing setting. 104 Figure 7. Multi-group SEM diagram with measurement models for the credibility predicted by the Rogerian factor, healing setting, and treatment rationale. 105 Figure 8. Multi-group SEM diagram with measurement models for the healing setting predicted by the Rogerian factor, credibility, and treatment rationale. 105 Figure C1. Example of the cross-sectional structural diagram with measurement models for the PsyPsych group. 110 viii Introduction Decades of research have established that psychotherapy is effective at alleviating psychological suffering and improving people’s lives (e.g., Seligman, 1995). The typical person receiving psychotherapy fares better than 79% of those who do not receive treatment (Cohen’s d = .80, Wampold & Imel, 2015; see Cohen, 1992, and Hemphill, 2003, for interpretation of effect sizes), and in comparison to medical practices, psychotherapy stands out as one of the most effective procedures available (Schnyder, 2009). However, much is still unknown about how and why psychotherapy works. Furthermore, despite considerable accumulation of knowledge, the effectiveness of psychotherapy treatments has not increased over time (Miller, Hubble, Chow & Seidel, 2013, p. 89). This may be in part attributable to the typical approach taken to the study of psychotherapy. Psychotherapy has been often conceptualized in a manner similar to medical treatments: as a set of specific procedures designed to ameliorate specific psychological deficits that cause corresponding mental disorders (i.e., the “Medical Model” approach; Wampold & Imel, 2015). For example, Cognitive-Behavioural Therapy (CBT) for depression purports to work by using cognitive restructuring to directly alter beliefs, which are thought to be causally linked to symptoms of depression (e.g., Beck, 1970; Sudak, 2012). Thus, in CBT, cognitive restructuring is assumed to be a key technique or “active ingredient” that, when applied competently, leads to improvement. This manner of thinking about psychotherapy has resulted in calls for the development, and privileging, of empirically supported therapies (ESTs; Chambless and Ollendick, 2001). ESTs are well-described sets of procedures designed for the treatment of specific disorders, which have been empirically demonstrated to be superior to “placebo” or 1 “supportive” therapies (i.e., therapies not designed to be therapeutic) or equivalent in efficacy to other ESTs (Chambless and Ollendick, 2001). It is necessary to seek empirical support for clinical practices, but the EST approach appears to answer Gordon Paul’s (1967) classic question (‘‘What treatment, by whom, is most effective for this individual with that specific problem, and under which set of circumstances?’’) by focusing primarily on the match between diagnosis and treatment. This medical model approach to the development of psychotherapy has
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