The item network and domain network of burnout in Chinese nurses

Wu et al. BMC Nursing (2021) 20:147 https://doi.org/10.1186/s12912-021-00670-8 RESEARCH Open Access The item network and domain network of burnout in Chinese nurses Lin Wu1†, Lei Ren1†, Yifei Wang1, Kan Zhang2, Peng Fang1, Xufeng Liu1, Qun Yang1, Xiuchao Wang1, Shengjun Wu1* and Jiaxi Peng3* Abstract 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 MBIGS. 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). * Correspondence: ; † Lin Wu and Lei Ren contributed equally to this work. 1 Department of Military Medical Psychology, Air Force Medical University, Xi’an 710032, China 3 College of Teachers, Chengdu University, Chengdu 610106, China Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wu et al. BMC Nursing (2021) 20:147 Page 2 of 11 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. Keywords: Nurse, Mental health, Burnout, Network analysis Background In 2019, the World Health Organization (WHO) recommended for the first time that “burnout” should be included into the International Classification of Disease 11th Edition; thus, indicating that burnout has become a common phenomenon in today’s society and has attracted widespread attention [1]. The term “burnout” was coined by Freudenberger. He believed that burnout is a state of emotional exhaustion that can easily develop during work; i.e., when the work requires a very high level of an individual’s energy and ability, the individual will develop an emotional exhaustion condition [2]. Maslach stated that burnout refers to emotional exhaustion, cynicism, and reduced satisfaction of employees in the professional field of service industry, which includes chronic negative emotions at work and coping with resource depletion in the face of stressors, also known as Burnout Syndrome [3, 4]. With the increasing pressure of medical industry, relatively insufficient proportion of nurses, stressful work, frequent three shifts, high work standards, high level of risks, many emergencies, and long-term high vigilance contribute to the high incidence of burnout [5, 6]. Burnout has a great impact on nurses’ work, which is manifested as decreased enthusiasm for the care of patients (emotional exhaustion) [7], being detached and insensate to the care and strained relationship with patients, intensified conflict with colleagues (cynicism) [8], meaningless nursing work, decline in self-esteem and passive neglect of work (decreased professional efficacy) [9]. Maslach Burnout Inventory-General Survey (MBI-GS) contains a total of 15 items in the three dimensions of emotional exhaustion, cynicism, and reduced personal satisfaction; all of these items are scored from 0 to 6, with 0 representing never and 6 representing very frequent. The score of each dimension is obtained by adding all the items included in each dimen (...truncated)