First impressions count: Therapists’ impression on patients’ motivation and helping alliance predicts psychotherapy dropout
Year: 2024
Type: article
Study design: Observational Study
Abstract: Objective: With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed. Method: We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples (N = 1,691 and N = 12,473) using baseline and initial process variables reported by patients and therapists. Results: Predictive accuracies of the two machine learning algorithms were similar and higher than for logistic regressions: Therapy dropout could be predicted with an AUC of .73 and .83 for Sample 1 and 2, respectively. The initial evaluation of patients’ motivation and the therapeutic alliance rated by the respective therapist were the most important predictors of dropout. Conclusions: Therapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators and therapists’ first impressions. Feature selection via regularization leads to higher predictive performances whereas non-linear or interaction effects are dispensable. The most promising point of intervention to reduce therapy dropouts seems to be patients’ motivation and the therapeutic alliance.
Authors: Jankowsky, Kristin, Zimmermann, Johannes, Jaeger, Ulrich, Mestel, Robert, Schroeders, Ulrich
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7106493569
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