arXiv cs.CL· 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·· 13 小时前AI 评分38
用大语言模型自动评估心理治疗依从性，实现可扩展的治疗督导
Scaling Therapy Supervision: Assessing Psychotherapy Fidelity Using Large Language Models
AI 导读
研究团队提出一套基于大语言模型的集成应用，可从真实长篇临床转录文本中自动完成心理治疗依从性评估并生成可执行的督导反馈。他们评测了三个开放权重模型在多种任务分解配置与推理策略下的表现，发现"证据优先"推理能提升依从性估计效果。该框架在独立随机对照试验中可靠区分 CBT 与非 CBT 会谈，心理健康专家用户研究也认为其生成的反馈具有可操作性。
正文 · 原文
Authors: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
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.
| Comments: | 24 pages, under review |
| Subjects: | Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.28772 [cs.CL] |
| (or arXiv:2610.28772v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28772 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Arvind Pillai [view email]
[v1]
Tue, 20 Oct 2026 18:09:44 UTC (244 KB)
来源：arXiv cs.CL · arxiv.org