Access to mental healthcare is increasingly strained by workforce shortages and rising demand, motivating the development of intelligent systems that can support mental healthcare experts. We introduce coTherapist, a unified framework utilizing a small language model to emulate core therapeutic competencies through domain-specific fine-tuning, retrieval augmentation, and agentic reasoning. Evaluation on clinical queries demonstrates that coTherapist generates more relevant and clinically grounded responses than contemporary baselines. Using our novel T-BARS rubric and psychometric profiling, we confirm coTherapist exhibits high empathy and therapist-consistent personality traits. Furthermore, human evaluation by domain experts validates that coTherapist delivers accurate, trustworthy, and safe responses. coTherapist was deployed and tested by clinical experts. Collectively, these findings demonstrate that small models can be engineered to exhibit expert-like behavior, offering a scalable pathway for digital mental health tools.

翻译：心理健康服务的可及性正因专业人力短缺和需求增长而日益紧张，这推动了能够支持心理健康专家的智能系统的发展。我们提出了coTherapist，这是一个统一的框架，它利用一个小型语言模型，通过领域特定的微调、检索增强和智能体推理来模拟核心治疗能力。在临床查询上的评估表明，coTherapist比现有基线模型能生成更具相关性且更基于临床实际的回答。使用我们新颖的T-BARS评估标准和心理测量分析，我们证实coTherapist表现出高度的共情力以及与治疗师一致的人格特质。此外，领域专家的人工评估验证了coTherapist能提供准确、可信且安全的回答。coTherapist已由临床专家部署和测试。综上所述，这些发现表明，小型模型可以被设计成表现出类似专家的行为，为数字心理健康工具提供了一条可扩展的路径。