推进心理评估:通过自由文本响应和语言模型BERT量化自我同情
Hirohito Okano1, Daisuke Kawahara2, Michio Nomura3
1Graduate School of Education, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
Scientific reports
|July 16, 2025
概括
这项研究引入了一种新的方法来测量使用自然语言处理 (NLP) 和参与者文本的自我同情. 这种基于文本的方法与传统的尺度有很高的相关性,为心理评估提供了一个不那么有偏见的替代方案.
科学领域:
- 心理学 心理学 心理学
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
背景情况:
- 自我同情对心理健康和福祉至关重要.
- 传统的自我同情测量依赖于像自我同情度量 (SCS) 这样的尺度.
- 对使用文本数据和NLP进行心理量化产生兴趣.
研究的目的:
- 开发和验证一种方法来量化自我同情,使用自由文本响应和BERT.
- 探索不同情况下的自我同情,思想和行为之间的关系.
主要方法:
- 收集了来自参与者的开放式免费文本,涉及在具有挑战性的情况下的想法和行为.
- 开发了一个回归模型,使用BERT来从文本中预测自怜得分 (SCS,CEAS).
- 基于文本的量化评估的与标准相关的有效性.
主要成果:
- 来自文本和BERT的自我同情得分与SCS得分高度相关.
- 确定了思维,行为和自我同情水平的情境差异.
- 证实了SCS得分,认知 (思想) 和行为之间的关系.
结论:
- 使用NLP对自我同情的基于文本的量化显示出有希望的有效性和与已建立的尺度的高相关性.
- 这种基于自然语言的方法可以减少与调查问卷相比的原始化和偏差.
- 未来的研究应该将这种方法应用于各种样本和其他心理结构.
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