Correlates of Burnout Profiles in Chinese Nurses: A Network Analysis of Psychological and Occupational Factors.

OBJECTIVES: This study aims to reveal the heterogeneous structure of job burnout among Chinese nurses, identify its key psychological and occupational influencing factors, and analyze the dynamic relationship network among various dimensions within these factors, with the expectation of providing a basis for formulating precise intervention measures.

METHODS: A cross-sectional survey of 1133 registered nurses from China was conducted. Participants completed validated scales of psychological resilience, perceived stress and burnout, and self-designed job-related questions. Latent class analysis (LCA) is applied to categorize nurses' manifestations of job burnout. Through multinomial regression analysis of influencing factors, key predictive factors that distinguish different burnout categories are determined. Finally, network analysis methods are applied to construct and analyze the correlation network among internal variables of key factors, identifying core nodes within the network.

RESULTS: The mean scores for the Maslach Burnout Inventory (MBI), Connor‒Davidson Resilience Scale (CD-RISC), and the Perceived Stress Scale (PSS) were 57.27 ± 19.47, 23.27 ± 9.35, and 17.37 ± 5.89, respectively. LCA revealed a 3-class solution: a mild burnout group (37.21%), a high emotional exhaustion group (47.49%), and a low personal accomplishment group (15.30%). Female nurses and those with higher resilience were less likely to belong to the high emotional exhaustion class, whereas unmarried status, working in internal medicine, low income, and low physical activity increased the risk. Network analysis identified PSS10 ("Difficulties piling up"), PSS1 ("Upset unexpected"), and RISC8 ("Coping with stress") as the most central nodes in terms of strength. Strong negative connections were observed between resilience and perceived stress clusters, with RISC4 ("See myself as a strong person") and PSS4 ("Handle personal problems") being notably linked. Job factors showed weaker but meaningful connections to stress nodes. Bootstrap difference tests confirmed that key centrality values were significantly higher than those of peripheral nodes (p < 0.05), and the network demonstrated high stability (CS-coefficient > 0.5).

CONCLUSIONS: There is significant group heterogeneity in job burnout among Chinese nurses. Psychological resilience, perceived stress, and job-related factors are core variables that lead to different patterns of burnout, and these variables have a closely intertwined dynamic relationship network within them. Some core dimensions may become key targets for intervention. The study offers a framework that progresses from classification to mechanism, informing the understanding and targeted intervention of nurse burnout.

IMPLICATION FOR NURSING MANAGEMENT: Applying the job demands-resources (JD-R) model to our network findings, we translate statistical centrality into management priority: perceived stress (a key demand) and psychological resilience (a key resource) are the most influential drivers of the burnout network. Effective nursing management should therefore pursue parallel strategies-reducing demands (e.g., workload calibration and shift predictability) while simultaneously strengthening resources (e.g., resilience workshops, peer debriefing, and supervisory support). In addition, our profile-based results call for differentiated protocols: emotional exhaustion signals demand overload and requires immediate operational relief, whereas low personal accomplishment signals resource scarcity and requires developmental engagement (e.g., training, mentoring, and recognition). Because these dimensions interact, even focused modifications in organizational infrastructure can produce widespread improvements.