Machine-Learning Prediction of Therapeutic Alliance Quality from Client Readiness to Change and Affective Dysregulation - Brown , Christopher; Pires , Lucas; Benskin , Terri L. (Journal of Assessment and Research in Applied Counseling (JARAC), 2026)
Objective: The aim of this study is to utilize and compare advanced machine-learning algorithms to predict the early quality of the therapeutic alliance based on a multidimensional assessment of client readiness to change and specific facets of affective dysregulation.Methods and Materials: A prospective observational design was utilized, involving n=452treatment-seeking adults initiating outpatient psychotherapy in Canada. Baseline predictor variables were collected prior to the first therapy session using the University of Rhode Island Change Assessment (URICA) and the Difficulties in Emotion Regulation Scale (DERS). The criterion variable, therapeutic alliance quality, was measured after the fourth session using the Working Alliance Inventory (WAI). Missing data (