Jing Fengshi, Cheng Mengyuan, Li Jing, He Chaocheng, Ren Hao, Zhou Jiandong, Zhou Hanchu, Xu Zhongzhi, Chen Weiming, Cheng Weibin
Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.
UNC Project-China, UNC Global, School of Medicine, The University of North Carolina, Chapel Hill, NC, United States.
Front Psychiatry. 2023 Apr 14;14:1119421. doi: 10.3389/fpsyt.2023.1119421. eCollection 2023.
Occupational burnout is a type of psychological syndrome. It can lead to serious mental and physical disorders if not treated in time. However, individuals tend to conceal their genuine feelings of occupational burnout because such disclosures may elicit bias from superiors. This study aims to explore a novel method for estimating occupational burnout by elucidating its links with social, lifestyle, and health status factors.
In this study 5,794 participants were included. Associations between occupational burnout and a set of features from a survey was analyzed using Chi-squared test and Wilcoxon rank sum test. Variables that are significantly related to occupational burnout were grouped into four categories: demographic, work-related, health status, and lifestyle. Then, from a network science perspective, we inferred the colleague's social network of all participants based on these variables. In this inferred social network, an exponential random graph model (ERGM) was used to analyze how occupational burnout may affect the edge in the network.
For demographic variables, age ( < 0.01) and educational background ( < 0.01) were significantly associated with occupational burnout. For work-related variables, type of position ( < 0.01) was a significant factor as well. For health and chronic diseases variables, self-rated health status, hospitalization history in the last 3 years, arthritis, cardiovascular diseases, high blood lipid, breast diseases, and other chronic diseases were all associated with occupational burnout significantly ( < 0.01). Breakfast frequency, dairy consumption, salt-limiting tool usage, oil-limiting tool usage, vegetable consumption, pedometer (step counter) usage, consuming various types of food (in the previous year), fresh fruit and vegetable consumption (in the previous year), physical exercise participation (in the previous year), limit salt consumption, limit oil consumption, and maintain weight were also significant factors ( < 0.01). Based on the inferred social network among all airport workers, ERGM showed that if two employees were both in the same occupational burnout status, they were more likely to share an edge ( < 0.0001).
The major limitation of this work is that the social network for occupational burnout ERGM analysis was inferred based on associated factors, such as demographics, work-related conditions, health and chronic diseases, and behaviors. Though these factors have been proven to be associated with occupational burnout, the results inferred by this social network cannot be warranted for accuracy.
This work demonstrated the feasibility of identifying people at risk of occupational burnout through an inferred colleague's social network. Encouraging staff with lower occupational burnout status to communicate with others may reduce the risk of burnout for other staff in the network.
职业倦怠是一种心理综合征。若不及时治疗，它会导致严重的身心障碍。然而，个体往往会隐瞒自己职业倦怠的真实感受，因为这种表露可能会引起上级的偏见。本研究旨在通过阐明职业倦怠与社会、生活方式和健康状况因素之间的联系，探索一种估计职业倦怠的新方法。
本研究纳入了5794名参与者。使用卡方检验和威尔科克森秩和检验分析职业倦怠与一项调查中的一系列特征之间的关联。与职业倦怠显著相关的变量被分为四类：人口统计学、工作相关、健康状况和生活方式。然后，从网络科学的角度，我们基于这些变量推断了所有参与者的同事社交网络。在这个推断出的社交网络中，使用指数随机图模型（ERGM）来分析职业倦怠如何影响网络中的边。
对于人口统计学变量，年龄（<0.01）和教育背景（<0.01）与职业倦怠显著相关。对于工作相关变量，职位类型（<0.01）也是一个重要因素。对于健康和慢性病变量，自我评估的健康状况、过去3年的住院史、关节炎、心血管疾病、高血脂、乳腺疾病和其他慢性病都与职业倦怠显著相关（<0.01）。早餐频率、乳制品消费、限盐工具使用、控油工具使用、蔬菜消费、计步器使用、各类食物消费（前一年）、新鲜水果和蔬菜消费（前一年）、体育锻炼参与（前一年）、限盐、控油和保持体重也是重要因素（<0.01）。基于对所有机场工作人员推断出的社交网络，ERGM显示，如果两名员工处于相同的职业倦怠状态，他们更有可能共享一条边（<0.0001）。
这项工作的主要局限性在于，用于职业倦怠ERGM分析的社交网络是基于相关因素推断出来的，如人口统计学、工作相关状况、健康和慢性病以及行为。尽管这些因素已被证明与职业倦怠有关，但这个社交网络推断出的结果准确性无法保证。
这项工作证明了通过推断出的同事社交网络识别有职业倦怠风险人群的可行性。鼓励职业倦怠程度较低的员工与他人交流，可能会降低网络中其他员工的倦怠风险。
