用大模型打造可交互的抑郁自测聊天机器人，效果可信且用户更信任。
Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening
- 用大模型+检索增强生成实现动态提问与实时澄清
- 132人测试显示评分一致性高（ICC=0.91），71%更信任聊天机器人
- 适合需要温和、结构化筛查的群体，尤其关注心理健康服务使用史者
静态工具如患者健康问卷-9（PHQ-9）虽能有效筛查抑郁，但缺乏互动性与适应性。我们开发了HopeBot，一个基于大语言模型（LLM）的聊天机器人，通过检索增强生成与实时澄清功能执行PHQ-9评估。在一项自身对照研究中，132名英中两国成年人完成了自填版与聊天机器人版。评分显示强一致性（ICC = 0.91；45%完全一致）。在75名提供反馈的参与者中，71%表示更信任聊天机器人，因其结构更清晰、解释更易懂、语气更支持性。平均评分（0-10）为：舒适度8.4，语音清晰度7.7，敏感话题处理7.6，建议帮助度7.4；后一项结果在就业状态和既往心理服务使用上差异显著（p < 0.05）。总体87.1%的用户愿重复使用或推荐。结果表明，基于语音的大模型聊天机器人可作为可扩展、低负担的常规抑郁筛查辅助手段。
原文摘要 · Abstract (English)
Static tools like the Patient Health Questionnaire-9 (PHQ-9) effectively screen depression but lack interactivity and adaptability. We developed HopeBot, a chatbot powered by a large language model (LLM) that administers the PHQ-9 using retrieval-augmented generation and real-time clarification. In a within-subject study, 132 adults in the United Kingdom and China completed both self-administered and chatbot versions. Scores demonstrated strong agreement (ICC = 0.91; 45% identical). Among 75 participants providing comparative feedback, 71% reported greater trust in the chatbot, highlighting clearer structure, interpretive guidance, and a supportive tone. Mean ratings (0-10) were 8.4 for comfort, 7.7 for voice clarity, 7.6 for handling sensitive topics, and 7.4 for recommendation helpfulness; the latter varied significantly by employment status and prior mental-health service use (p < 0.05). Overall, 87.1% expressed willingness to reuse or recommend HopeBot. These findings demonstrate voice-based LLM chatbots can feasibly serve as scalable, low-burden adjuncts for routine depression screening.
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