Utility of salivary cortisol profile as a predictive biomarker in nurses' turnover risk: a preliminary study.

Abstract

Predicting nurse turnover risk is crucial due to the global nursing shortage; however, existing predictors, such as fatigue and burnout, lack objectivity. Salivary cortisol is a non-invasive marker of stress and fatigue, but its utility in predicting nurse turnover risk is unknown. We examined whether salivary cortisol profiles across three different day shifts in a month are predictors of the extent of nurses' reluctance to stay in their current jobs. This preliminary longitudinal study followed forty female nurses who engaged in shift work at a university hospital for 3 months. Data at enrollment were collected including demographics, working conditions, chronic fatigue (the Japanese version of the Occupational Fatigue/Exhaustion Recovery Scale), and burnout (Japanese Burnout scale). Salivary cortisol was measured before the three different day shifts (after awakening) during the first month, and the means of these measurements were used as the cortisol profile. The extent of reluctance to stay was assessed using the numerical rating scale at 3 months. Among the forty female nurses (mean [SD] age, 28.3 [5.1]), all completed follow-up and were included in the analysis. The cortisol profile was associated with the extent of reluctance to stay (P = 0.017), and this association was significant despite adjustments for chronic fatigue and burnout (P = 0.005). A multiple regression model with chronic fatigue, burnout, and job tenure explained 41.5% of the variation in reluctance to stay. When the cortisol profile was added to this model, the association of the cortisol profile was significant (P = 0.006) with an R2 of 0.529 (ΔR2 = 0.114). This preliminary study conducted in an actual clinical setting indicated the potential of the salivary cortisol profile across three different day shifts in a month to predict nurses' reluctance to stay in their current jobs. The combination of subjective indicators and the cortisol profile would be useful in predicting nurses' turnover risk.