自我污名并非单一概念，但通用共情是：面向吸毒者的人物条件化LLM支持
Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs
AI总结：
针对吸毒者自我污名表达异质性问题，提出基于潜在剖面分析的四人物类型学，并用序列贝叶斯和循环神经网络从有限发帖历史中恢复人物类型，发现人物匹配响应虽能实现行为转变，但临床专家更偏好通用共情。
摘要：
自我污名预测吸毒者（PWUD）的治疗回避和脱离，但旨在提供支持的对话系统通常将自我污名表达视为统一信号。我们提出了一项三阶段概念验证研究，采用人物感知方法进行LLM支持。对Reddit上1,174名自我污名表达者的指标级特征进行潜在剖面分析（LPA），得出一个四人物类型学，并通过保留的行为和语言特征进行验证。序列贝叶斯和循环神经网络分类器从有限发帖历史中恢复这些人物类型，显著优于批处理和少样本LLM基线（30条帖子时macro-F1=0.74）。八位临床专家对三种当代LLM的评估揭示了错位：人物匹配响应成功实现了目标行为转变，但评分者整体上偏好人物中性基线的通用共情。我们的发现表明，整体共情判断与临床对齐的响应设计可能相互背离，评估基于LLM的污名支持需要能够分解这两者的评分标准。
英文摘要：
Self-stigma predicts treatment avoidance and disengagement among people who use drugs (PWUD), yet conversational systems aiming to provide support typically treat self-stigma expression as a uniform signal. We present a three-phase, proof-of-concept study of a persona-aware approach to LLM support. Latent Profile Analysis (LPA) on indicator-level features from 1,174 self-stigma expressors on Reddit yields a four-persona typology validated against held-out behavioral and linguistic features. Sequential Bayesian and recurrent neural classifiers recover these personas from limited posting histories, substantially outperforming batch and few-shot LLM baselines (macro-F1 = 0.74 at 30 posts). Evaluation by eight clinical experts across three contemporary LLMs revealed a misalignment: persona-matched responses successfully achieved targeted behavioral shifts, yet raters holistically preferred the generic empathy of the persona-neutral baseline. Our findings suggest that holistic empathy judgments and clinically-aligned response design can pull in opposite directions, and that evaluating LLM-based stigma support requires rubrics capable of decomposing the two.
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。
