Relationship between burnout and turnover intention among nurses: a network analysis

BACKGROUND: Nurse burnout and turnover intention significantly impact global healthcare systems, especially intensified by the COVID-19 pandemic. This study employs network analysis to explore these phenomena, providing insights into the interdependencies and potential intervention points within the constructs of burnout and turnover intention among nurses. METHODS: A cross-sectional study was conducted with 560 nurses from three tertiary hospitals in Hangzhou, China. Data were collected via online questionnaires, including the Maslach Burnout Inventory-General Survey (MBI-GS) and the Turnover Intention Questionnaire (TIQ). Network analysis was performed using Gaussian graphical models to construct the network model and calculate related metrics. RESULTS: The network analysis revealed that items related to personal accomplishment and emotional exhaustion were central, indicating significant roles in influencing nurses' turnover intentions. Specifically, perceived meaningful work and self-efficacy emerged as pivotal nodes, suggesting that enhancing these can mitigate turnover intentions. The network's stability and accuracy were confirmed through bootstrapping methods, emphasizing the robustness of the findings. CONCLUSION: The study underscores the importance of addressing nurse burnout by focusing on core elements like personal accomplishment and self-efficacy to reduce turnover intentions. These insights facilitate targeted interventions that could improve nurse retention and stability within healthcare systems. Future research should expand to multi-center studies to enhance the generalizability of these findings.