Trajectories and Predictors of Emotional Exhaustion in Clinical Nurses in the Context of Healthcare Crisis: A Case Study in Xi'an, China

Abstract

Abstract Background In response to the diminishing toxicity and fatality of the novel coronavirus, China implemented a policy shift at the end of 2022 to relax its control measures pertaining to the COVID-19 pandemic. Consequently, a rapid surge in community-level infections ensued, exerting a pronounced strain on the medical and healthcare systems and posing significant challenges and pressures for healthcare workers. Objective This study investigated the growth trajectory of emotional exhaustion and its predictors in clinical nurses in the context of healthcare crisis. Methods A total of 422 frontline clinical nursing staff from Xi’an, China, were followed up three times over two months, and data from the final 358 clinical nurses were analyzed. The growth mixed model (GMM) was used to identify the trajectory categories, and the predictive factors of the trajectory types were analyzed by logistic regression. The relationship of the trajectory types of emotional exhaustion with psychological capital were analyzed by variance analysis. Results The best-fit growth mixture modeling revealed three class models: Class 1 characterized by high and increasing levels of emotional exhaustion, Class 2 characterized by moderate and decreasing levels of emotional exhaustion, and Class 3 characterized by low and decreasing levels of emotional exhaustion. These classes accounted for 9.78%, 83.52%, and 6.70% of clinical nurses, respectively. The results of the univariate analysis indicated that age, years of working experience, gender, past participation in emergency public health event rescue, and sleep quality were associated with the trajectory of emotional exhaustion. Different groups of nursing personnel had varying levels of psychological capital, with higher levels of emotional exhaustion associated with lower levels of psychological capital. Logistic regression analysis revealed that gender, past participation in emergency public health event rescue, and sleep quality were independent predictors of the emotional exhaustion trajectory. Female clinical nurses who had not participated in emergency public health event rescue and had poor sleep quality were more likely to experience persistent high levels of emotional exhaustion. Conclusion Our study demonstrated the heterogeneity of emotional exhaustion among frontline clinical nursing personnel in coping with healthcare system overload, and the significant impact of gender, past participation in emergency public health event rescue, and sleep quality on the development of emotional exhaustion in clinical nurses. Healthcare administrators should prioritize their attention to clinical nurses who are at a higher risk of developing a persistent high emotional exhaustion pattern and provide targeted interventions.