使用压力检查数据预测心理健康相关的病假:开发和验证工作场所风险模型
Shunsuke Inoue1,2, Tomohisa Nagata1, Naoto Ito1,2
1Department of Occupational Health Practice and Management, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health1-1 Iseigaoka, Yahatanishi-ku, Kitakyushu 807-8555, Japan.
Journal of occupational health
|February 26, 2026
概括
一个新的预测模型准确地识别了面临心理健康相关病假风险的员工. 这个模型是使用日本开发的.
科学领域:
- 职业健康 职业健康 职业健康
- 精神病学是一个精神病学.
- 数据科学数据科学数据科学
背景情况:
- 精神健康相关的病假在职业环境中构成了重大挑战.
- 现有的识别高风险员工的方法,如高压力分类,可能缺乏准确性.
- 日本的压力检查计划为开发更准确的预测工具提供了潜在的数据来源.
研究的目的:
- 开发和验证心理健康相关病假的预测模型.
- 将日本压力检查计划的数据与员工人事记录整合起来.
- 将新模型的预测性能与传统的高压力分类进行比较.
主要方法:
- 对14家公司集团 (2020-2024) 的员工进行了一项回顾性队列研究.
- 一个混合效应的物流回归模型被开发并使用2020-2023和2024年的数据进行验证.
- 使用LASSO逻辑回归精细化了变量选择,模型性能通过布赖尔得分和AUC进行评估.
主要成果:
- 最终的模型确定了七个关键预测因素:年轻年龄,焦虑,抑郁,睡眠障碍,工作控制能力低,工作适应性差,同事支持率低.
- 该模型在开发过程中显示出良好的区分 (AUC=0.708) 和校准 (布赖尔分数=0.0079).
- 时间验证显示性能有所改善 (AUC=0.819,布赖尔得分=0.0026),显著优于传统的高压力分类.
结论:
- 开发的预测模型显示了强大的有效性,用于识别面临心理健康相关病假风险的员工.
- 该模型提供了定量风险分层,增强了职业健康方面的决策.
- 这些发现支持将此类模型整合到职场健康计划中,以进行积极的风险管理.
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