预测工人的压力:使用工作方式特征应用高性能算法
Hiroki Iwamoto1, Saki Nakano1, Ryotaro Tajima1
1Shionogi & Co., Ltd., Osaka, Japan.
JMIR AI
|August 2, 2024
概括
这项研究开发了一种新的算法,使用个性化的工作数据来预测员工的压力. 根据远程工作率对员工进行分组,提高了压力预测的准确性,并确定了不同工作风格的独特压力因素.
科学领域:
- 职业健康 职业健康 职业健康
- 数据科学数据科学数据科学
- 心理健康 心理健康
背景情况:
- 远程工作率和工作特征与员工的压力有关.
- 现有的压力预测模型缺乏个性化和高性能.
- 个人化生活方式数据集成用于压力建模尚未得到充分探索.
研究的目的:
- 开发一种新的,高性能算法来预测员工的压力.
- 创建一个个性化的压力预测模型,以适应个人工作风格.
- 评估远程工作率对压力预测准确性的影响.
主要方法:
- 未来的观察性研究涉及190名员工超过12周.
- 收集的数据包括可穿戴设备指标 (睡眠,活动,心率),班次工作细节和每周问卷 (K6,行为,错过的饭菜).
- 基于类似的工作风格特征和远程工作率,使用邻里集群 (N=20) 开发了一个预测模型.
主要成果:
- 拟议的方法实现了曲线下的面积 (AUC) 为0.84.
- 根据远程工作率,特征对压力预测的重要性有很大差异.
- 高度远程工作群体:与跳过午餐,工作时间偏差,心率变化和睡眠时间相关的压力.
- 低远程工作群体:与工作到达时间,心率水平,心率变化和卡路里消耗相关的压力.
结论:
- 根据工作方式 (远程工作率) 将员工分为群组,显著改善了压力预测性能.
- 社区集群方法通过跨远程工作层次的独特贡献特征证明了其有效性.
- 结合工作方式特征的个性化压力预测模型是有效的.
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