Evaluation of burnout among stay-behind healthcare workers during the current Omicron wave of COVID-19 in Taizhou, China

Abstract

BackgroundSince February 2022, a new Omicron wave of COVID-19 emerged in Shanghai, China. Many healthcare workers came to Shanghai from hospitals of other parts of China as aid workers. Hospitals in areas with mild COVID-19 outbreaks will inevitably be understaffed, it is likely to cause job burnout of stay-behind healthcare workers. Stay-behind healthcare workers were those who had not been dispatched to support COVID-19 prevention and control in other regions. This study was designed to evaluate the burnout among stay-behind healthcare workers in the current COVID-19 Omicron wave in Taizhou, China.MethodsA population-based, anonymous, cross-sectional online survey was designed in the Wen-Juan Xing platform. The survey was sent to all stay-behind healthcare workers of the hospital (n = 1739) from April 29 to May 3, 2022. The Maslach Burnout Inventory-General Survey (MBI-GS) was used for the burnout survey. For univariate analysis, the χ2 test and one way ANOVA were used to assess differences in categorical variables and continuous variables, respectively. The effect of independent associated risk factors on each type of burnout was examined using the multinomial logistic regression model.ResultsA total of 434 participants completed the survey invitation effectively. A total of 71.2% of stay-behind healthcare workers experienced burnout during COVID-19, including 54.8% experiencing mild to moderate burnout and 16.4% experiencing severe burnout. Night shift, depression, social support, positive coping and number of children appeared to be significantly related to mild to moderate burnout. Night shift, depression, social support, positive coping, number of children, professional title, and anxiety appeared to be significantly related to severe burnout.ConclusionJob burnout among stay-behind healthcare workers was an important problem during the current Omicron wave of COVID-19. Night shift, depression, social support, positive coping, and number of children were associated with mild to moderate and severe burnout. Anxiety and professional title were associated with severe burnout.