Network Analysis of Burnout and Safety Competence Among Oncology Nurses: A Secondary Study to Identify Bridge Targets...

Occupational burnout poses a persistent threat to nurses' mental health and the quality of patient care. Emerging evidence indicates that burnout is not a uniform phenomenon but manifests in distinct psychological patterns. Yet, how these diverse burnout experiences interact with safety-related factors is insufficiently understood. Network analysis offers a systems-level perspective to uncover interconnections among psychological and safety variables and to pinpoint potential bridge nodes for targeted interventions.

This study sought to map the network architecture linking psychological and safety-related factors among nurses across different burnout profiles, to identify profile-specific central and bridge nodes, and to examine their associations with safety behaviors.

A total of 2092 nurses were included. This study was a secondary analysis based on a previously established dataset in which three distinct burnout profiles were identified using latent profile analysis: the High Achievement Stable Group (Class 1, 70.3%), the High Efficiency Contradictory Group (Class 2, 6.6%), and the High Pressure Adaptive Group (Class 3, 23.1%). Psychological-safety networks were estimated for both the overall sample and each subgroup using the EBICglasso model. Centrality and bridging indices were computed via expected influence and bridge expected influence, followed by network comparison tests to evaluate structural variations across profiles.

In the overall network, "skills" (B4) exhibited the greatest centrality, whereas "personal accomplishment" (A3) and "knowledge" (B1) consistently functioned as pivotal bridge nodes across profiles. Although bridge configurations differed slightly among classes, A3 and B1 remained the principal connectors integrating psychological and safety communities. Significant structural differences were detected between Classes 2 and 1 (M test, p < 0.001) and between Classes 3 and 1 (M test, p < 0.001; S test, p = 0.002), with pronounced discrepancies in the edge patterns surrounding A3 and B1.

The burnout-safety networks revealed distinct structural configurations across nurse subgroups. Identifying profile-specific bridge nodes offers practical guidance for precision interventions that enhance safety behaviors and foster occupational resilience.