从利克尔特尺度到大型语言模型:验证计算方法对未来自我连续性的心理评估
Yosef Sokol1,2, Marianne Goodman1,3
1VISN 2 Mental Illness Research, Education and Clinical Center (MIRECC), James J. Peters Veterans Affairs Medical Center.
Journal of personality assessment
|October 30, 2025
概括
大型语言模型 (LLM) 提供了一种新的方法来评估心理概念,如未来自我连续性 (FSC). 这种新的自然语言处理 (NLP) 方法对研究和临床实践具有前景.
科学领域:
- 心理评估 心理评估
- 在行为科学中的人工智能.
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 传统的利克特尺度在测量复杂的心理构造方面存在局限性.
- 大型语言模型 (LLM) 为先进的评估方法提供了新的机会.
- 未来自我连续性 (Future Self-Continuity,FSC) 是一个关键的构造,与现在和未来自我之间的感知联系有关.
研究的目的:
- 引入和验证一种新的基于LLM的心理学评估方法.
- 应用这种新的NLP方法来测量未来的自我连续性 (FSC).
- 探索基于LLM的FSC评估的临床实用性,特别是与自杀思想有关.
主要方法:
- 在164名参与者的采访成绩单上使用LLM (Claude 3.5 Sonnet) 进行NLP.
- 量化NLP-FSC得分得出并与未来自连续性问卷 (FSCQ) 相比较.
- 通过不同模型版本的复制证实了LLM的稳定性;使用自杀行为调查问卷修订 (SBQR) 探索了临床效用.
主要成果:
- 通过Bland-Altman分析,NLP-FSC得分与FSCQ (r = 0.57) 显示出显著的收有效性和可接受的协议.
- 复制研究证实了基于LLM的方法在不同模型版本中的稳定性 (模型间r = 0.840.91).
- NLP评估捕获了超出FSCQ的自杀企图可能性的独特差异,表明了潜在的临床相关性.
结论:
- 经过验证的基于LLM的NLP方法为评估未来自我连续性 (FSC) 提供了一种细微而强大的方法.
- 这种方法推进了心理测量,在研究和临床环境中都有潜在的应用.
- 这种方法对识别有风险的人来说是有前途的,特别是在自杀思想方面.
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