以人力资源数据为基础的机器学习与工作福利指标相结合,预测行为健康员工周转率.
1School of Social Work, Indiana University Indianapolis, Indianapolis.
Psychiatric services (Washington, D.C.)
|January 30, 2026
概括
使用历史的人力资源数据和工作幸福感调查的机器学习模型可以预测社区行为健康组织的员工周转率,改善员工保留力度.
科学领域:
- 组织心理学 组织心理学
- 在人力资源领域的数据科学.
- 劳动力分析工作人员分析.
背景情况:
- 对于社区行为健康组织 (CBHO) 来说,员工保留至关重要.
- 预测员工流动对于有效的保留策略至关重要.
- 现有的方法可能是劳动密集型或缺乏预测准确性.
研究的目的:
- 开发和验证一种识别高风险流动的员工的方法.
- 将历史的人力资源 (HR) 数据与工作福利指标相结合,以改善预测.
- 评估机器学习 (ML) 模型在预测员工流动方面的有效性.
主要方法:
- 利用在历史的人力资源数据 (2011年以来) 上训练的ML模型来估计当前员工流动率的概率.
- 通过调查,从现有员工收集了12个工作幸福感指标.
- 采用后勤回归来测试ML衍生的概率和幸福指标的联合预测能力.
主要成果:
- 该研究包括303名CBHO员工;24%的人自愿离开.
- ML衍生出的营业额概率 ([公式:见文本]) 预测了实际的营业额.
- 添加三个幸福指标 (职业进步,期望调整,监督重要性) 显著改善了预测准确性 (AUC从0.63到0.76).
结论:
- 建立了一种可行的,不那么劳动密集型的方法来预测员工周转率.
- 在历史的人力资源数据上训练的ML模型有效地识别有风险的员工.
- 基于调查的就业福利指标显著增加了营业额预测的准确性.
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