Therapist Effects on and Predictors of Non‐Consensual Dropout in Psychotherapy

Abstract

BACKGROUND: Whereas therapist effects on outcome have been a research topic for several years, the influence of therapists on premature treatment termination (dropout) has hardly been investigated. Since dropout is common during psychological treatment, and its occurrence has important implications for both the individual patient and the healthcare system, it is important to identify the factors associated with it. METHOD: Participants included 707 patients in outpatient psychotherapy treated by 66 therapists. Multilevel logistic regression models for dichotomous data were used to estimate the impact of therapists on patient dropout. Additionally, sociodemographic variables, symptoms, personality style and treatment expectations were investigated as potential predictors. RESULTS: It was found that 5.7% of variance in dropout could be attributed to therapists. The therapist's effect remained significant after controlling for patient's initial impairment. Furthermore, initial impairment was a predictor of premature termination. Other significant predictors of dropout on a patient level were male sex, lower education status, more histrionic and less compulsive personality style and negative treatment expectations. CONCLUSIONS: The findings indicate that differences between therapists influence the likelihood of dropout in outpatient psychotherapy. Further research should focus on variables, which have the potential to explain these inter-individual differences between therapists (e.g., therapist's experience or self-efficacy). Copyright © 2016 John Wiley & Sons, Ltd. KEY PRACTITIONER MESSAGES: There are substantial differences between therapists concerning their average dropout rates. At the patient level, higher initial impairment, male sex, lower education, less compulsive personality style, more histrionic personality style and low treatment expectations seem to be risk factors of non-consensual treatment termination. Psychometric feedback during the course of treatment should be used to identify patients who are at risk for dropout.

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