“这真的是人类同伴支持者吗？”：同伴支持者与专家在LLM支持互动中的错位
"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
AI总结：
研究通过混合方法评估LLM模拟求助者、生成建议和实时情绪可视化系统，发现同伴支持者与心理健康专家在识别求助信号和给予建议上存在关键错位，凸显标准化培训需求。
摘要：
心理健康是全球日益关注的议题，促使人们探索AI驱动的解决方案以扩大心理社会支持的可及性。基于亲身经历的同伴支持为专业护理提供了有价值的补充。然而，培训、有效性和定义的差异性引发了对质量、一致性和安全性的担忧。大型语言模型（LLMs）为增强同伴支持互动提供了新机遇，尤其是在实时文本交互中。我们提出并评估了一个AI支持系统，包含LLM模拟的求助者（client）、上下文敏感的LLM生成建议（suggestions）和实时情绪可视化。通过两项混合方法研究，与12名同伴支持者和6名心理健康专家（即专家）共同考察了系统的有效性及对实践的影响。两组参与者均认可其提升培训质量和互动质量的潜力。然而，我们发现了一个关键张力：同伴支持者积极参与互动，而专家则持续指出同伴支持者回应中的关键问题，如遗漏求助信号和过早提供建议。这种错位凸显了当前同伴支持培训的潜在局限性，尤其是在情绪高度紧张、安全性和遵循最佳实践至关重要的情境中。我们的研究结果强调了标准化、基于心理学的培训的必要性，特别是在同伴支持全球推广的背景下。同时，它们展示了LLM支持系统如何为这一发展提供支撑——前提是精心设计并接受专家监督。本研究为心理健康领域负责任AI整合的讨论，以及LLM在增强同伴支持护理中不断演变的角色做出了贡献。
英文摘要：
Mental health is a growing global concern, prompting interest in AI-driven solutions to expand access to psychosocial support. Peer support, grounded in lived experience, offers a valuable complement to professional care. However, variability in training, effectiveness, and definitions raises concerns about quality, consistency, and safety. Large Language Models (LLMs) present new opportunities to enhance peer support interactions, particularly in real-time, text-based interactions. We present and evaluate an AI-supported system with an LLM-simulated distressed client (SimClient), context-sensitive LLM-generated suggestions (Suggestions), and real-time emotion visualisations. 2 mixed-methods studies with 12 peer supporters and 6 mental health professionals (i.e., experts) examined the system's effectiveness and implications for practice. Both groups recognised its potential to enhance training and improve interaction quality. However, we found a key tension emerged: while peer supporters engaged meaningfully, experts consistently flagged critical issues in peer supporter responses, such as missed distress cues and premature advice-giving. This misalignment highlights potential limitations in current peer support training, especially in emotionally charged contexts where safety and fidelity to best practices are essential. Our findings underscore the need for standardised, psychologically grounded training, especially as peer support scales globally. They also demonstrate how LLM-supported systems can scaffold this development--if designed with care and guided by expert oversight. This work contributes to emerging conversations on responsible AI integration in mental health and the evolving role of LLMs in augmenting peer-delivered care.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
