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arXiv · 2610.28772

Scaling Therapy Supervision: Assessing Psychotherapy Fidelity Using Large Language Models

Arvind Pillai·Alexis Roach·Jason Holden·Varsha D Badal·Subigya Nepal·Yu Yvonne Wu·Yuliang Chen·Sudarshan Regmi·Sarah Masud Preum·Nicholas C Jacobson·Nikhil Singh·Eric Granholm·Colin Depp·Andrew Campbell
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Abstract

Psychotherapy fidelity assessment measures how faithfully a therapist adheres to a treatment protocol, a critical determinant of positive patient outcomes. However, fidelity assessment is bottlenecked by manual expert review, rendering the process labor-intensive and unscalable. To scale therapy supervision and support clinician self-learning, we present an integrated application of large language models (LLMs) that automates psychotherapy fidelity workflows and generates actionable supervisory feedback from authentic, long-form clinical transcripts. We evaluate three open-weight models across multiple task decomposition configurations and reasoning strategies, demonstrating that evidence-first reasoning improves fidelity estimation. Furthermore, we establish the framework's external construct validity on an independent randomized controlled trial (RCT), where it reliably distinguishes Cognitive Behavioral Therapy (CBT) from non-CBT sessions across core therapeutic skills. Through a user study that evaluates the generated feedback, mental health experts indicate that the pipeline synthesizes actionable clinical feedback. Ultimately, our work offers a practical, scalable solution toward bridging the community mental health supervision gap.

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Arvind Pillai, Alexis Roach, Jason Holden, Varsha D Badal, Subigya Nepal, Yu Yvonne Wu, Yuliang Chen, Sudarshan Regmi, Sarah Masud Preum, Nicholas C Jacobson, Nikhil Singh, Eric Granholm, Colin Depp, Andrew Campbell. 2026-10-20. Scaling Therapy Supervision: Assessing Psychotherapy Fidelity Using Large Language Models. https://arxiv.org/abs/2610.28772

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