使用机器学习方法检查国家卫生机构的营业额率
Sezen O Onal1, Morgan Pak, Jonathon P Leider
1Author Affiliation: Center for Public Health Systems, Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, Minnesota (Dr Onal, Ms Pak, Dr Leider).
Journal of public health management and practice : JPHMP
|November 17, 2025
概括
机器学习模型有效地预测公共卫生工作人员的流动性意图. 提高工作满意度显著降低了离开的意图,特别是在职业生涯早期和中期的员工中.
科学领域:
- 公共卫生工作人员分析
- 机器学习在人力资源中的应用
- 组织心理学是组织心理学.
背景情况:
- 公共卫生部门的高周转率挑战了服务连续性和知识保留.
- 流行后的因素,如倦怠和不满,加剧了劳动力不稳定.
- 机器学习 (ML) 提供了改善营业额意向预测的潜力.
研究的目的:
- 应用ML技术来分析公共卫生工作人员的营业额意图预测因素.
- 模拟提高工作场所满意度对员工留守的影响.
- 确定国家卫生机构雇员中营业额意向的关键驱动因素.
主要方法:
- 利用公共卫生工作人员兴趣和需求调查数据的4个浪潮 (2014-2024年).
- 训练有素的拉索回归,随机森林和梯度提升ML模型来预测离开的意图.
- 评估变量重要性和模拟的营业额减少从满意度的改善.
主要成果:
- ML模型显示出强大的预测性能 (AUC 0.78-0.85).
- 工作满意度是主要预测因素,其次是组织和薪酬满意度.
- 模拟显示,营业额大幅减少,满意度略有提高,特别是在职业生涯早期/中期的员工中.
结论:
- ML有效地识别了公共卫生工作人员流动意向的关键驱动因素.
- 工作满意度,组织气候和薪酬对于留住员工至关重要.
- 在公共卫生组织中,ML工具可以实现主动的,数据驱动的保留策略.
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