使用人力资源数据来预测社区心理健康员工的周转率:机器学习方法的预测和解释
1Department of Psychology, Indiana University Indianapolis, Indianapolis, Indiana, USA.
International journal of mental health nursing
|July 4, 2024
概括
机器学习使用人力资源数据准确预测心理健康员工的流动. 关键预测因素包括工作史,人口统计和客户特征,为目标保留策略提供信息.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 应用机器学习应用机器学习
- 人力资源管理 人力资源管理
背景情况:
- 在社区精神卫生中心,员工流动是一个重大挑战.
- 预测和减轻营业额对于保持服务连续性和质量至关重要.
研究的目的:
- 应用机器学习 (ML) 方法来预测心理健康员工在12个月内的周转率.
- 通过使用人力资源数据,确定这一特定劳动力中营业额的关键预测因素.
主要方法:
- 利用社区精神卫生中心621名员工的人力资源数据.
- 应用了六种ML模型:逻辑回归,弹性网,随机森林 (RF),梯度增强机 (GBM),神经网络和支持向量机.
- 采用图形和统计工具来解释预测关系和相互作用.
主要成果:
- 随机森林 (RF) 和梯度增强机 (GBM) 显示出优异的预测性能 (AUC > 0.8).
- 确定了包括过去工作年数,工作时间,工资,年龄和员工类型在内的重要预测因素.
- 发现了心理健康工作人员的独特预测因素,例如培训时间和精神分裂症患者比例.
结论:
- 机器学习有效地使用人力资源数据预测心理健康员工流动.
- 已识别的预测因素,包括非线性和交互效应,为开发有针对性的保留策略提供了洞察力.
- 需要进一步的研究来完善心理健康劳动力保留的预测模型.
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