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A huge number of clinical studies and meta-analyses have shown that psychotherapy is effective on average. However, not every patient profits from psychotherapy and some patients even deteriorate in treatment. Due to this result and the restricted generalization of clinical studies to clinical practice, a more patient-focused research strategy has emerged. The question whether a particular treatment works for an individual case is the focus of this paradigm. The use of repeated assessments and the feedback of this information to therapists is a major ingredient of patient-focused research. Improving patient outcomes and reducing dropout rates by the use of psychometric feedback seems to be a promising path. Therapists seem to differ in the degree to which they make use of and profit from such feedback systems. This dissertation aims to better understand therapist differences in the context of patient-focused research and the impact of therapists on psychotherapy. Three different studies are included, which focus on different aspects within the field:
Study I (Chapter 5) investigated how therapists use psychometric feedback in their work with patients and how much therapists differ in their usage. Data from 72 therapists treating 648 patients were analyzed. It could be shown that therapists used the psychometric feedback for most of their patients. Substantial variance in the use of feedback (between 27% and 52%) was attributable to therapists. Therapists were more likely to use feedback when they reported being satisfied with the graphical information they received. The results therefore indicated that not only patient characteristics or treatment progress affected the use of feedback.
Study II (Chapter 6) picked up on the idea of analyzing systematic differences in therapists and applied it to the criterion of premature treatment termination (dropout). To answer the question whether therapist effects occur in terms of patients’ dropout rates, data from 707 patients treated by 66 therapists were investigated. It was shown that approximately six percent of variance in dropout rates could be attributed to therapists, even when initial impairment was controlled for. Other predictors of dropout were initial impairment, sex, education, personality styles, and treatment expectations.
Study III (Chapter 7) extends the dissertation by investigating the impact of a transfer from one therapist to another within ongoing treatments. Data from 124 patients who agreed to and experienced a transfer during their treatment were analyzed. A significant drop in patient-rated as well as therapist-rated alliance levels could be observed after a transfer. On average, there seemed to be no difficulties establishing a good therapeutic alliance with the new therapist, although differences between patients were observed. There was no increase in symptom severity due to therapy transfer. Various predictors of alliance and symptom development after transfer were investigated. Impacts on clinical practice were discussed.
Results of the three studies are discussed and general conclusions are drawn. Implications for future research as well as their utility for clinical practice and decision-making are presented.
There is no longer any doubt about the general effectiveness of psychotherapy. However, up to 40% of patients do not respond to treatment. Despite efforts to develop new treatments, overall effectiveness has not improved. Consequently, practice-oriented research has emerged to make research results more relevant to practitioners. Within this context, patient-focused research (PFR) focuses on the question of whether a particular treatment works for a specific patient. Finally, PFR gave rise to the precision mental health research movement that is trying to tailor treatments to individual patients by making data-driven and algorithm-based predictions. These predictions are intended to support therapists in their clinical decisions, such as the selection of treatment strategies and adaptation of treatment. The present work summarizes three studies that aim to generate different prediction models for treatment personalization that can be applied to practice. The goal of Study I was to develop a model for dropout prediction using data assessed prior to the first session (N = 2543). The usefulness of various machine learning (ML) algorithms and ensembles was assessed. The best model was an ensemble utilizing random forest and nearest neighbor modeling. It significantly outperformed generalized linear modeling, correctly identifying 63.4% of all cases and uncovering seven key predictors. The findings illustrated the potential of ML to enhance dropout predictions, but also highlighted that not all ML algorithms are equally suitable for this purpose. Study II utilized Study I’s findings to enhance the prediction of dropout rates. Data from the initial two sessions and observer ratings of therapist interventions and skills were employed to develop a model using an elastic net (EN) algorithm. The findings demonstrated that the model was significantly more effective at predicting dropout when using observer ratings with a Cohen’s d of up to .65 and more effective than the model in Study I, despite the smaller sample (N = 259). These results indicated that generating models could be improved by employing various data sources, which provide better foundations for model development. Finally, Study III generated a model to predict therapy outcome after a sudden gain (SG) in order to identify crucial predictors of the upward spiral. EN was used to generate the model using data from 794 cases that experienced a SG. A control group of the same size was also used to quantify and relativize the identified predictors by their general influence on therapy outcomes. The results indicated that there are seven key predictors that have varying effect sizes on therapy outcome, with Cohen's d ranging from 1.08 to 12.48. The findings suggested that a directive approach is more likely to lead to better outcomes after an SG, and that alliance ruptures can be effectively compensated for. However, these effects
were reversed in the control group. The results of the three studies are discussed regarding their usefulness to support clinical decision-making and their implications for the implementation of precision mental health.