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The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis

BACKGROUND: Although poor mental well-being (MW) has been documented among individuals experiencing burnout during the coronavirus-19 (COVID-19) pandemic, little is known about the complex interrelationship between different components of MW and burnout. This study investigates this relationship amo...

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Autores principales: Chen, Chen, Li, Fengzhan, Liu, Chang, Li, Kuiliang, Yang, Qun, Ren, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399609/
https://www.ncbi.nlm.nih.gov/pubmed/36033796
http://dx.doi.org/10.3389/fpubh.2022.919692
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author Chen, Chen
Li, Fengzhan
Liu, Chang
Li, Kuiliang
Yang, Qun
Ren, Lei
author_facet Chen, Chen
Li, Fengzhan
Liu, Chang
Li, Kuiliang
Yang, Qun
Ren, Lei
author_sort Chen, Chen
collection PubMed
description BACKGROUND: Although poor mental well-being (MW) has been documented among individuals experiencing burnout during the coronavirus-19 (COVID-19) pandemic, little is known about the complex interrelationship between different components of MW and burnout. This study investigates this relationship among medical staff during the COVID-19 pandemic through network analysis. METHODS: A total of 420 medical staff were recruited for this study. Components of MW were measured by the 14-item Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and components of burnout were measured by a 15-item Maslach Burnout Inventory-General Survey (MBI-GS) Questionnaire. Network structure was constructed via network analysis. Bridge variables were identified via the bridge centrality index. RESULTS: The edges across two communities (i.e., MW community and burnout community) are almost negative, such as edge MW2 (“Useful”) – B14 (“Worthwhile”) and edge MW1 (“Optimistic about future”) – B13 (“Happy”). The edges within each community are nearly positive. In the MW community, components MW1 (“Optimistic about future”) and MW6 (“Dealing with problems”) have the lowest bridge centrality. And in the community of burnout, components B13 (“Happy”) and B14 (“Worthwhile”) have the lowest bridge expected influence. CONCLUSION: We present the first study to apply the network approach to model the potential pathways between distinct components of MW and burnout. Our findings suggest that promoting optimistic attitudes and problem-solving skills may help reduce burnout among medical staff during the pandemic.
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spelling pubmed-93996092022-08-25 The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis Chen, Chen Li, Fengzhan Liu, Chang Li, Kuiliang Yang, Qun Ren, Lei Front Public Health Public Health BACKGROUND: Although poor mental well-being (MW) has been documented among individuals experiencing burnout during the coronavirus-19 (COVID-19) pandemic, little is known about the complex interrelationship between different components of MW and burnout. This study investigates this relationship among medical staff during the COVID-19 pandemic through network analysis. METHODS: A total of 420 medical staff were recruited for this study. Components of MW were measured by the 14-item Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and components of burnout were measured by a 15-item Maslach Burnout Inventory-General Survey (MBI-GS) Questionnaire. Network structure was constructed via network analysis. Bridge variables were identified via the bridge centrality index. RESULTS: The edges across two communities (i.e., MW community and burnout community) are almost negative, such as edge MW2 (“Useful”) – B14 (“Worthwhile”) and edge MW1 (“Optimistic about future”) – B13 (“Happy”). The edges within each community are nearly positive. In the MW community, components MW1 (“Optimistic about future”) and MW6 (“Dealing with problems”) have the lowest bridge centrality. And in the community of burnout, components B13 (“Happy”) and B14 (“Worthwhile”) have the lowest bridge expected influence. CONCLUSION: We present the first study to apply the network approach to model the potential pathways between distinct components of MW and burnout. Our findings suggest that promoting optimistic attitudes and problem-solving skills may help reduce burnout among medical staff during the pandemic. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399609/ /pubmed/36033796 http://dx.doi.org/10.3389/fpubh.2022.919692 Text en Copyright © 2022 Chen, Li, Liu, Li, Yang and Ren. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Chen, Chen
Li, Fengzhan
Liu, Chang
Li, Kuiliang
Yang, Qun
Ren, Lei
The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis
title The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis
title_full The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis
title_fullStr The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis
title_full_unstemmed The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis
title_short The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis
title_sort relations between mental well-being and burnout in medical staff during the covid-19 pandemic: a network analysis
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399609/
https://www.ncbi.nlm.nih.gov/pubmed/36033796
http://dx.doi.org/10.3389/fpubh.2022.919692
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