边缘型人格障碍的计算现象学：对LLM模拟专家人设与人类临床专家的比较评估
Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts
AI总结：
本研究比较了LLM模拟专家与人类专家在边缘型人格障碍定性分析中的表现，发现模型在某些方面与人类难以区分，并能识别人类遗漏的主题，展示了AI增强分析的潜力。
摘要：
基于人类主导的主题分析对住院治疗的边缘型人格障碍患者的生活故事访谈，本研究考察了大型语言模型（OpenAI的GPT、Google的Gemini和Anthropic的Claude）支持定性临床分析的能力。这些模型通过混合程序进行评估。研究A涉及盲审和非盲审的专家法官在现象学和临床心理学中的评估，包括语义一致性、输出重叠的Jaccard系数、多维有效性评分（可信度、连贯性和结果的实质性和数据基础）。在研究B中，神经方法被用来将人类和模型创建的主题描述嵌入到二维向量空间中，以提供人类和模型语义和语言风格差异的计算度量。在研究C中，进行了互补的非专家评估，以检验主题详尽性对人类作者身份感知和内容有效性的影响。所有三个研究的结果显示，模型与人类分析的重叠程度各不相同，模型在某种程度上与人类难以区分，并且识别了人类研究人员最初遗漏的主题。这些发现突显了AI增强的主题定性分析的变异性及其潜力，能够缓解人类解释偏差并提高敏感性。
英文摘要：
Building on a human-led thematic analysis of clinical life-story interviews (> 150,000 words) with inpatients with Borderline Personality Disorder, this study examines the capacity of large language models (OpenAI's GPT, Google's Gemini, and Anthropic's Claude) to support qualitative clinical analysis. The models' interpretative potential was evaluated using a mixed-methods approach. Study A involved blinded and non-blinded judges in phenomenology and clinical psychology. The experts assessed the validity of AI-generated content using semantic congruence, Jaccard coefficients, and multidimensional validity ratings, including credibility, coherence, the substantiveness of results, and grounding in qualitative data. In Study B, neural methods were used to embed human- and model-generated theme descriptions in a multidimensional vector space. This approach provided an objectified computational measure of the difference between human and model semantics and linguistic style. In Study C, complementary non-expert evaluations were conducted (N=115) to examine the influence of thematic verbosity on the perception of human authorship and content validity. Overall, the results of AI analysis showed a highly variable overlap (0-58%) with the human interpretation, while all models identified themes originally omitted by human researchers, proving their capacity to mitigate human bias. At the thematic level, external evaluators were unable to reliably distinguish human-authored themes from those generated by AI. In terms of content validity assessed against raw data by high-level experts in the blinded mode, the performance of Gemini 2.5 Pro was indistinguishable from that of humans. The comparison of semantic vector embeddings showed that its style was also the closest to humans.
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。
