Association of machine-learning-rated supportive counseling skills with psychotherapy outcome.

Abstract

Objective: This study applied a machine-learning-based skill assessment system to investigate the association between supportive counseling skills (empathy, open questions, and reflections) and treatment outcomes. We hypothesized that higher empathy and higher use of open questions and reflections would be associated with greater symptom reduction.

Method: We used a data set with 2,974 sessions, 610 clients, and 48 therapists collected from a university counseling center, which included 845,953 rated therapist statements. Client outcome was routinely monitored by the Counseling Center Assessment of Psychological Symptoms Instruments. Therapists' skills were measured via computer by a bidirectional-long-short-term-memory-based system that rated use of supportive counseling skills. We used multilevel modeling to separate the between-therapist and the within-therapist associations of the skills and outcome.

Results: Use of open questions and reflections was associated with client symptom reduction between therapists but not within therapists. We did not find significant associations between therapist empathy and client symptom reduction but found that empathy was negatively associated with clients' baseline symptom level within therapists.

Conclusions: Therapist exploration of clients' experience and expression of understanding may be important skills that are associated with clients' better outcomes. This study highlights the importance of support counseling skills, as well as the potential of machine-learning-based measures in psychotherapy research. We discuss the limitations of the study, including the limitations related to the speaker recognition system and potential reasons for the lack of association between empathy and client outcome. (PsycInfo Database Record (c) 2025 APA, all rights reserved).