Predicting nursing turnover with catastrophe theory.

Area of Science:

• Nursing
• Healthcare Management
• Predictive Analytics

Background:

• The global nursing shortage necessitates accurate identification of staff at risk for turnover.
• Traditional linear models for predicting nursing turnover are complex and yield suboptimal results.
• Nonlinear modeling approaches offer enhanced simplicity and predictive accuracy.

Purpose of the Study:

• To compare the predictive accuracy of an innovative nonlinear catastrophe model against a traditional linear model for nursing turnover.
• To identify key variables that accurately predict registered nurse turnover.

Main Methods:

• A correlational, longitudinal cohort prospective study was conducted with 1033 Registered Nurses in the US Midwest.
• Data analysis involved a cusp catastrophe model, a dynamic, four-dimensional nonlinear model.
• Predictor variables included organizational commitment, anticipated turnover, and job tension.

Main Results:

• The cusp catastrophe model demonstrated 80.4% overall predictability and 53.6% accuracy in predicting actual nurse terminations.
• Predictive accuracy was highest for nurses with less than five years of experience.
• Organizational commitment and anticipated turnover were significant predictors, while job tension was not.

Conclusions:

• Nonlinear catastrophe models are effective tools for predicting nursing turnover.
• These findings support the use of catastrophe models in future nursing research to mitigate turnover and improve workforce stability.