The item network and domain network of burnout in Chinese nurses

BACKGROUND: As a common social phenomenon, nurses’ occupational burnout has a high incidence rate, which seriously affects their mental health and nursing level. The current assessment mostly uses the total score model and explores the influence of external factors on burnout, while the correlation between burnout items or dimensions is less explored. Ignoring the correlation between the items or dimensions may result in a limited understanding of nurse occupational burnout. This paper explores the item and dimension network structure of the Maslach Burnout Inventory-General Survey (MBI-GS) in Chinese nurses, so as to gain a deeper understanding of this psychological construct and identify potential targets for clinical intervention. METHODS: A total of 493 Chinese nurses were recruited by cluster sampling. All participants were invited to complete the survey on symptoms of burnout. Network analysis was used to investigate the item network of MBI-GS. In addition, community detection was used to explore the communities of MBI-GS, and then network analysis was used to investigate the dimension network of MBI-GS based on the results of community detection. Regularized partial correlation and non-regularized partial correlation were used to describe the association between different nodes of the item network and dimension network, respectively. Expected influence and predictability were used to describe the relative importance and the controllability of nodes in both the item and dimension networks. RESULTS: In the item network, most of the strongly correlated edges were in the same dimension of emotional exhaustion (E), cynicism (C) and reduced professional efficacy (R), respectively. E5 (Item 5 of emotional exhaustion, the same below) “I feel burned out from my work”, C1 “I have become more callous toward work since I took this job”, and R3 “In my opinion, I am good at my job” had the highest expected influence (z-scores = 0.99, 0.81 and 0.94, respectively), indicating theirs highest importance in the network. E1 “I feel emotionally drained from my work” and E5 had the highest predictability (E1 = 0.74, E5 = 0.74). It shows that these two nodes can be interpreted by their internal neighbors to the greatest extent and have the highest controllability in the network. The spinglass algorithm and walktrap algorithm obtained exactly the same three communities, which are consistent with the original dimensions of MBI-GS. In the dimension network, the emotional exhaustion dimension was closely related to the cynicism dimension (weight = 0.65). CONCLUSIONS: The network model is a useful tool to study burnout in Chinese nurses. This study explores the item and domain network structure of nurse burnout from the network perspective. By calculating the relevant indicators, we found that E5, C1, and R3 were the most central nodes in the item network and cynicism was the central node in the domain network, suggesting that interventions aimed at E5, C1, R3 and cynicism might decrease the overall burnout level of Chinese nurses to the greatest extent. This study provides potential targets and a new way of thinking for the intervention of nurse burnout, which can be explored and verified in clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12912-021-00670-8.