Wu Lin, Ren Lei, Wang Yifei, Zhang Kan, Fang Peng, Liu Xufeng, Yang Qun, Wang Xiuchao, Wu Shengjun, Peng Jiaxi
Department of Military Medical Psychology, Air Force Medical University, Xi'an, 710032, China.
Tangdu Hospital, Air Force Medical University, Xi'an, 710038, China.
BMC Nurs. 2021 Aug 17;20(1):147. doi: 10.1186/s12912-021-00670-8.
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.
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.
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).
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.
护士职业倦怠作为一种常见的社会现象，发病率较高，严重影响其心理健康和护理水平。目前的评估大多采用总分模型，探讨外部因素对倦怠的影响，而对倦怠条目或维度之间的相关性探讨较少。忽视条目或维度之间的相关性可能导致对护士职业倦怠的理解有限。本文探讨中文版马氏倦怠量表通用版（MBI-GS）在中国护士中的条目和维度网络结构，以便更深入地理解这一心理结构，并确定临床干预的潜在靶点。
采用整群抽样法共招募493名中国护士。邀请所有参与者完成倦怠症状调查。采用网络分析研究MBI-GS的条目网络。此外，采用社区检测法探索MBI-GS的社区，然后根据社区检测结果，采用网络分析研究MBI-GS的维度网络。分别用正则化偏相关和非正则化偏相关描述条目网络和维度网络中不同节点之间的关联。用预期影响和可预测性描述条目网络和维度网络中节点的相对重要性和可控性。
在条目网络中，大多数强相关边分别处于情感耗竭（E）、玩世不恭（C）和职业效能降低（R）的同一维度。E5（情感耗竭的第5项，下同）“我感觉工作让我精疲力竭”、C1“自从从事这项工作以来，我对工作变得更加冷漠”和R3“在我看来，我擅长我的工作”具有最高的预期影响（z分数分别为0.99、0.81和0.94），表明它们在网络中具有最高的重要性。E1“我感觉工作让我情绪枯竭”和E5具有最高的可预测性（E1 = 0.74，E5 = 0.74）。这表明这两个节点在很大程度上可以由其内部邻域解释，并且在网络中具有最高的可控性。自旋玻璃算法和随机游走算法得到了完全相同的三个社区，这与MBI-GS的原始维度一致。在维度网络中，情感耗竭维度与玩世不恭维度密切相关（权重 = 0.65）。
网络模型是研究中国护士倦怠的有用工具。本研究从网络角度探讨了护士倦怠的条目和领域网络结构。通过计算相关指标，我们发现E5、C1和R3是条目网络中最核心的节点，玩世不恭是领域网络中的核心节点，这表明针对E5、C1、R3和玩世不恭的干预措施可能在最大程度上降低中国护士的整体倦怠水平。本研究为护士倦怠的干预提供了潜在靶点和新的思维方式，可在临床实践中进行探索和验证。
