地域に根差した行動保健組織における従業員の定着率向上：人事データに基づく機械学習と職務上の幸福度指標の組み合わせによる離職予測

Identifying employees at risk for turnover is critical to supporting staff retention in community behavioral health organizations (CBHOs). The authors propose a method that uses machine-learning (ML) models trained with historical human resources (HR) data in combination with survey-based indicators of job well-being to predict employee turnover.

The ML models trained with historical HR data (starting in 2011) were applied to current employees' HR data to estimate turnover probability, labeled [Formula: see text]. Additionally, 12 job well-being indicators were collected from current employees via organization-wide surveys, and their actual turnover status was determined 12 months later. Logistic regressions were used to test the predictability of [Formula: see text] combined with the job well-being indicators for predicting future turnover.

The study included 303 CBHO employees (mean±SD age=43.2±13.2 years); 72% were female, and 60% were White. Seventy-two (24%) left their CBHO voluntarily by the time turnover outcome data were extracted. The [Formula: see text] predicted actual turnover, and adding three critical job well-being indicators (career advancement opportunities, expectation alignment, and importance of supervision) significantly improved prediction accuracy (the area under the curve increased from 0.63 to 0.76). Particularly [Formula: see text] (OR=2.15) and career advancement opportunities (OR=0.61) significantly predicted employee turnover when the other well-being indicators were held constant.

This study provides a viable and less labor-intensive method for identifying employees at high risk for turnover by applying ML models trained on historical HR data and identifies additional survey data that could augment predictions.