Abstract：
This study used machine learning (ML) to predict mental health employees\' turnover in the following 12 months using human resources data in a community mental health centre. The data contain 621 employees\' information (e.g., demographics, job information and client information served by employees) hired between 2011 and 2021 (56.5% turned over during the study period). Six ML methods (i.e., logistic regression, elastic net, random forest [RF], gradient boosting machine [GBM], neural network and support vector machine) were used to predict turnover, along with graphical and statistical tools to interpret predictive relationship patterns and potential interactions. The result suggests that RF and GBM led to better prediction according to specificity, sensitivity and area under the curve (>0.8). The turnover predictors (e.g., past work years, work hours, wage, age, exempt status, educational degree, marital status and employee type) were identified, including those that may be unique to the mental health employee population (e.g., training hours and the proportion of clients with schizophrenia diagnosis). It also revealed nonlinear and nonmonotonic predictive relationships (e.g., wage and employee age), as well as interaction effects, such that past work years interact with other variables in turnover prediction. The study indicates that ML methods showed the predictability of mental health employee turnover using human resources data. The identified predictors and the nonlinear and interactive relationships shed light on developing new predictive models for turnover that warrant further investigations.
摘要：
这项研究使用机器学习（ML）来预测心理健康员工在接下来的12个月中的离职率，使用社区心理健康中心的人力资源数据。数据包含621名员工的信息(例如，人口统计，由员工提供的工作信息和客户信息）在2011年至2021年之间雇用（在研究期间移交了56.5％）。六种ML方法(即，逻辑回归，弹性网，随机森林[RF]，梯度增压机[GBM]，神经网络和支持向量机)用于预测营业额，以及图形和统计工具来解释预测关系模式和潜在的相互作用。结果表明，根据特异性，RF和GBM可以更好地预测，灵敏度和曲线下面积(>0.8)。营业额预测因子(例如，过去的工作年,工作时间,工资,年龄,豁免状态,教育程度,婚姻状况和员工类型)被确定，包括那些可能是精神卫生雇员群体特有的(例如，培训时间和精神分裂症诊断客户的比例)。它还揭示了非线性和非单调的预测关系(例如，工资和员工年龄)，以及相互作用的影响，这样，过去的工作年限与营业额预测中的其他变量相互作用。研究表明，ML方法使用人力资源数据显示了心理健康员工离职的可预测性。已确定的预测因子以及非线性和互动关系为开发新的营业额预测模型提供了启示，这些模型值得进一步研究。
