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Prediction of Behavioral Health Employee Turnover With HR Data-Based Machine Learning Combined With Job Well-Being

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Machine learning models using historical HR data and job well-being surveys can predict employee turnover in community behavioral health organizations, improving staff retention efforts.

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Area of Science:

  • Organizational Psychology
  • Data Science in HR
  • Workforce Analytics

Background:

  • Staff retention is crucial for community behavioral health organizations (CBHOs).
  • Predicting employee turnover is essential for effective retention strategies.
  • Existing methods may be labor-intensive or lack predictive accuracy.

Purpose of the Study:

  • To develop and validate a method for identifying employees at high risk of turnover.
  • To combine historical human resources (HR) data with job well-being indicators for improved prediction.
  • To assess the efficacy of machine learning (ML) models in predicting employee turnover.

Main Methods:

  • Utilized ML models trained on historical HR data (since 2011) to estimate current employee turnover probability.
  • Collected 12 job well-being indicators from current employees via surveys.
  • Employed logistic regression to test the combined predictive power of ML-derived probability and well-being indicators.

Main Results:

  • The study included 303 CBHO employees; 24% voluntarily left.
  • The ML-derived turnover probability ([Formula: see text]) predicted actual turnover.
  • Adding three well-being indicators (career advancement, expectation alignment, supervision importance) significantly improved prediction accuracy (AUC from 0.63 to 0.76).

Conclusions:

  • A viable, less labor-intensive method for predicting employee turnover was established.
  • ML models trained on historical HR data effectively identify at-risk employees.
  • Survey-based job well-being indicators significantly augment turnover prediction accuracy.