使用语言建模测量弹性:一种用于观察弹性的计算方法.
Syeda Mahwish1,2, Ryan L Boyd3, Vasudha Varadarajan1
1Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.
Journal of traumatic stress
|January 28, 2026
概括
我们使用语言建模 (ReLM) 开发了弹性,以测量语言中的心理弹性. 较高的ReLM分数与随着时间的推移,创伤后应激障碍症状的显著改善有关.
科学领域:
- 心理学 心理学 心理学
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 在不良事件发生后,弹性对心理健康至关重要.
- 通过语言动态地测量弹性是具有挑战性的.
- 现有的方法可能无法捕捉到弹性的细微,多方面的性质.
研究的目的:
- 开发和验证一种新的自然语言处理 (NLP) 方法,即使用语言建模 (ReLM) 的弹性,用于测量心理弹性.
- 评估ReLM衍生弹性得分与创伤后应激障碍 (PTSD) 症状轨迹之间的关系.
主要方法:
- 开发了ReLM,一种基于原型分析的NLP模型,通过九个方面对弹性进行概念化.
- 利用语言模型将来自9/11袭击响应者的语音邮件数据嵌入语义空间.
- 为每个弹性方面计算了与原型陈述的共弦相似性,以得出分数.
- 应用主要组件分析和测量不变性测试用于模型验证.
- 通过使用ANOVA分析了跨性四分位数的PTSD症状轨迹.
主要成果:
- ReLM确定了一个单一的潜伏因素,解释了弹性方面56%的差异.
- 测量不变性测试证实了ReLM在数据子集中的一致性.
- 在弹性四分位数 (p=.002) 中观察到PTSD症状轨迹的显著差异.
- 具有高弹性的人在四年内在PTSD症状中表现出最显著的改善.
结论:
- ReLM提供了一种基于NLP的理论基础的方法,用于通过语言测量心理弹性.
- 该模型捕捉了故事中反映的动态心理过程.
- 通过ReLM,可以对弹性及其对心理健康结果的影响进行更细微和更动态的评估.
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