Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis
BackgroundOccupational 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 aim...
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1119421/full |
| _version_ | 1797847265877950464 |
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| author | Fengshi Jing Fengshi Jing Mengyuan Cheng Mengyuan Cheng Jing Li Chaocheng He Hao Ren Jiandong Zhou Hanchu Zhou Hanchu Zhou Zhongzhi Xu Weiming Chen Weibin Cheng Weibin Cheng |
| author_facet | Fengshi Jing Fengshi Jing Mengyuan Cheng Mengyuan Cheng Jing Li Chaocheng He Hao Ren Jiandong Zhou Hanchu Zhou Hanchu Zhou Zhongzhi Xu Weiming Chen Weibin Cheng Weibin Cheng |
| author_sort | Fengshi Jing |
| collection | DOAJ |
| description | BackgroundOccupational 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.MethodsIn 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.ResultsFor demographic variables, age (p < 0.01) and educational background (p < 0.01) were significantly associated with occupational burnout. For work-related variables, type of position (p < 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 (p < 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 (p < 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 (p < 0.0001).LimitationThe 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.ConclusionThis 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. |
| first_indexed | 2024-04-09T18:08:32Z |
| format | Article |
| id | doaj.art-facbee25cd9a4d46900672a0df649626 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-04-09T18:08:32Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Psychiatry |
| spelling | doaj.art-facbee25cd9a4d46900672a0df6496262023-04-14T05:06:16ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402023-04-011410.3389/fpsyt.2023.11194211119421Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysisFengshi Jing0Fengshi Jing1Mengyuan Cheng2Mengyuan Cheng3Jing Li4Chaocheng He5Hao Ren6Jiandong Zhou7Hanchu Zhou8Hanchu Zhou9Zhongzhi Xu10Weiming Chen11Weibin Cheng12Weibin Cheng13Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, ChinaUNC Project-China, UNC Global, School of Medicine, The University of North Carolina, Chapel Hill, NC, United StatesInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, ChinaUNC Project-China, UNC Global, School of Medicine, The University of North Carolina, Chapel Hill, NC, United StatesGuangzhou Baiyun International Airport Co., Ltd, Guangzhou, ChinaSchool of Information Management, Wuhan University, Wuhan, ChinaInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, ChinaNuffield Department of Medicine, University of Oxford, Oxford, United KingdomSchool of Traffic and Transportation Engineering, Central South University, Changsha, ChinaSchool of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, ChinaSchool of Public Health, Sun Yat-Sen University, Guangzhou, ChinaHealth Medicine Department, Guangdong Second Provincial General Hospital, Guangzhou, ChinaInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, ChinaSchool of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, ChinaBackgroundOccupational 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.MethodsIn 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.ResultsFor demographic variables, age (p < 0.01) and educational background (p < 0.01) were significantly associated with occupational burnout. For work-related variables, type of position (p < 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 (p < 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 (p < 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 (p < 0.0001).LimitationThe 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.ConclusionThis 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1119421/fulloccupational burnoutnetwork sciencehealth managementexponential random graph modelsocial networks |
| spellingShingle | Fengshi Jing Fengshi Jing Mengyuan Cheng Mengyuan Cheng Jing Li Chaocheng He Hao Ren Jiandong Zhou Hanchu Zhou Hanchu Zhou Zhongzhi Xu Weiming Chen Weibin Cheng Weibin Cheng Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis Frontiers in Psychiatry occupational burnout network science health management exponential random graph model social networks |
| title | Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis |
| title_full | Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis |
| title_fullStr | Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis |
| title_full_unstemmed | Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis |
| title_short | Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis |
| title_sort | social lifestyle and health status characteristics as a proxy for occupational burnout identification a network approach analysis |
| topic | occupational burnout network science health management exponential random graph model social networks |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1119421/full |
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