Factors influencing nurse fatigue during COVID-19: regression vs. fuzzy-set qualitative comparative analysis - Zhang, Huanyu; Liu, Zhixin; Liu, Junping; Feng, Yajie; Zou, Dandan; Zhao, Juan; Wang, Chen; Wang, Nan; Liu, Xinru; Wu, Lin; Liu, Zhaoyue; Liang, Libo; et al. (Frontiers in Public Health, 2023)
Background Nurses during COVID-19 who face significant stress and high infection risk are prone to fatigue, affecting their health and quality of patient care. A cross- sectional study of 270 nurses who went to epidemic area to support anti-epidemic was carried out via online survey during the COVID-19 pandemic on November 2021. Methods A web-based cross-sectional survey of 270 nurses in China who traveled to Heihe City in Heilongjiang Province to combat the novel coronavirus epidemic. The researchers collected information on sociodemographic variables, anxiety, transition shock, professionalism, collaboration, hours of work per day, and fatigue. Regression and fuzzy-set Quality Comparative Analysis (fsQCA) evaluated the factors’ impact on the nurses’ fatigue. Results Regression analysis showed that the psychological variables significant for fatigue, transition shock (β = 0.687, p < 0.001) and anxiety (β = 0.757, p < 0.001) were positively associated with fatigue, professionalism (β = −0.216, p < 0.001) was negatively associated with fatigue, and among the work-related variables, cooperation (β = −0.262, p < 0.001) was negatively related to fatigue. FsQCA analysis showed that combined effects of work hours, anxiety, and nurses’ educational status caused most of the fatigue (raw coverage = 0.482, consistency = 0.896). Conclusion This study provides two main findings, the one is the greater transition shock experienced during COVID-19 in a new environment, low levels of professionalism, anxiety, and poor nursing teamwork situations lead anti-epidemic nurses to increased fatigue. Second, the fsQCA results showed that anxiety is sufficient for fatigue and that nurses’ educational status, daily working hours, and anxiety are the most effective combination of factors.