Mapping the association between burnout and intent to leave: a network analysis of public health workers in China

The COVID-19 pandemic has exacerbated burnout and intent to leave among public health workers worldwide, yet the central and bridge relationships between burnout symptoms and intent to leave remain poorly understood. This study applies network analysis to map the interconnections between burnout symptoms and intent to leave among Chinese public health workers, aiming to identify candidate symptoms that could inform targeted interventions. A nationwide cross-sectional online survey was conducted among 1,887 public health workers across China (October-November 2024). Burnout was measured using the Maslach Burnout Inventory (MBI), and intent to leave with O’Reilly’s 4-item scale. A regularized partial correlation network was estimated, and centrality and bridge centrality indices were computed to identify core and bridging symptoms. Bootstrap methods were used to evaluate the stability and accuracy of the network. Among the 1,887 public health workers, 59.1% were male, 59.4% were > 30 years, and 51.8% were public health physicians. The network model revealed dense interconnections between burnout symptoms and intent to leave. The three most central symptoms were severe exhaustion (MBI5), declining work interest (MBI6), and doubts about work value (MBI8). Notably, MBI6 and MBI8 also showed the strongest bridge centrality linking the depersonalization dimension to intent to leave. The network demonstrated good stability and accuracy across all indices. Three symptoms—severe exhaustion (MBI5), declining work interest (MBI6), and doubts about work value (MBI8)—emerged as most central, with these two depersonalization symptoms (MBI6 and MBI8) serving as primary bridges connecting burnout to intent to leave. These findings highlight specific symptom targets for intervention strategies aimed at reducing burnout-related intent to leave among public health workers.