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

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.

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.

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.

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.