Factors influencing nurses’ post-traumatic growth during the COVID-19 pandemic: Bayesian network analysis

OBJECTIVE: During the COVID-19 pandemic, nurses, especially if females and working in intensive care units or emergencies unit, were much more at risk than other health-workers categories to develop malaise and acute stress symptoms. This study aimed to examine the nurses’ post-traumatic growth and associated influencing factors during the COVID-19 pandemic. METHODS: A cross-sectional study using an online survey was conducted at Henan Provincial People’s Hospital to gather data from nurses. A set of questionnaires was used to measure the participants’ professional identity, organizational support, psychological resilience and post-traumatic growth. Univariate, correlation, and multiple linear regression analyses were used to determine significant factors influencing post-traumatic growth. A theoretical framework based on the Bayesian network was constructed to understand post-traumatic growth and its associated factors comprehensively. RESULTS: In total, 1,512 nurses participated in the study, and a moderate-to-high level of post-traumatic growth was reported. After screening, the identified variables, including psychological counseling, average daily working hours, average daily sleep duration, professional identity, organizational support, and psychological resilience, were selected to build a Bayesian network model. The results of Bayesian network showed that professional identity and psychological resilience positively affected post-traumatic growth directly, which was particularly pronounced in low- and high-scoring groups. While organizational support positively affected post-traumatic growth indirectly. CONCLUSION: Although this study identified a moderate-to-high level of nurses’ post-traumatic growth, proactive measures to improve psychological resilience fostered by professional identity and organizational support should be prioritized by hospitals and nursing managers.