用于句子完成测试的智能代理:创建和应用在抑郁评估中
Yuchen Huang1,2, Mengxiao Lei2, Hanyu Zhang3
1Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Frontiers in psychology
|August 28, 2025
概括
这项研究使用大型语言模型 (LLM) 来通过句子完成测试 (SCT) 来评估抑郁症. 基于LLM的代理显示出高可靠性和有效性,为心理查提供了一种创新的方法.
科学领域:
- 心理评估
- 人工智能在心理健康方面
- 临床心理学
背景情况:
- 传统的自我报告问卷有误解和社会偏见.
- 句子完成测试 (SCT) 是一种具有潜力的投影技术,但在手动评分和高成本方面面临挑战.
- 大型语言模型 (LLM) 的进展为克服传统心理评估方法的局限性提供了机会.
研究的目的:
- 根据SCT框架开发和验证使用LLM进行中国大学生抑郁症评估的智能药物.
- 与手动评分和已确定的压力尺度相比,评估LLM驱动的SCT代理的可靠性,有效性和一致性.
- 在心理查期间评估代理人识别无效反应的能力.
主要方法:
- 专为抑郁症评估而设计的一套SCT项目.
- 将SCT的理论框架与LLM功能整合起来,创建一个自建的智能代理.
- 包括可靠性和有效性分析 (克朗巴赫的阿尔法,探索性因子分析),标准相关性 (贝克抑郁 inventory,自评抑郁量表) 和无效响应检测指标 (F1,准确性,精度,回忆) 在内的三个进步实证研究.
主要成果:
- 智能代理证明了良好的可靠性 (克朗巴赫的α=0.89-0.92) 和有效性.
- 与手动SCT评分 (r = 0.96) 的高一致性以及与贝克抑郁 inventory (r = 0.89) 和自我评分抑郁量表 (r = 0.85) 的显著相关性.
- 该药物在识别无效反应方面取得了很高的表现 (F1 = 0.94,精度 = 0.99,精度 = 0.99,回忆 = 0.90).
结论:
- 这项研究成功地展示了使用大型语言模型评估抑郁症的句子完成测试的智能转换.
- 基于LLM的代理提供了可靠,有效和高效的替代传统方法,克服了手动评分和潜在偏差的局限性.
- 这种创新为心理评估提供了新的学术和实践途径,特别是在大规模查环境中.
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