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

Abstract

BackgroundAlthough 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.MethodsA 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.ResultsThe 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.ConclusionWe 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.

Adjusting intervention strategies for mental health of COVID-19 patients: A network analysis based on a survey in Omicron-infected patients

“…Network analysis can be adopted to detect the symptomto-symptom relationships, and the network model is useful to assess the importance of each symptom in a disorder (12)(13)(14). Currently, it is widely used to study the association between depression and anxiety in different populations during COVID-19 epidemic (13)(14)(15)(16)(17).…”

“…Network analysis can be adopted to detect the symptomto-symptom relationships, and the network model is useful to assess the importance of each symptom in a disorder (12)(13)(14). Currently, it is widely used to study the association between depression and anxiety in different populations during COVID-19 epidemic (13)(14)(15)(16)(17). As far as we know, few studies have adopted network analysis to explore the relationship between depression and anxiety symptoms in COVID-19 patients, especially when the number of asymptomatic patients is increased.…”

Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis

“…The finding was consistent with our previous research used network analysis discussing the associations between burnout and neuroticism. For “Indifferent”-“Doubt significance” and “Exhausted”-“Used up” in our network models, Chen et al reported the strong relations of “Exhausted”-“Used up” and “Contributing”–“Good at the job” in exploring the connections between burnout and mental health among medical staff [ 60 ].The former was consistent with our studies. The results implied the heavy psychological burden among ICU nurses during COVID-19.…”

Association of burnout with depression in pharmacists: A network analysis

“…Taken together, although the relationship between the depression and burnout has been extensively studied at the disorder/syndrome level, there is still a lack of more fine-grained symptom-level research. To solve this problem, network analysis can be used as a novel approach to explore the complex symptom-level relationship between mental disorders ( 37 ). Several recent studies have explored the symptom-level associations between symptoms of burnout and depression using network analysis.…”