Introduction: Frontline nurses fighting against the epidemic were under great psychological stress. However, there is a lack of studies assessing the prevalence rates of anxiety, depression, and insomnia among frontline nurses after the full liberalization of COVID-19 in China. This study demonstrates the impact of the full liberalization of COVID-19 on the psychological issues and the prevalence rate and associated factors of depressive symptoms, anxiety, and insomnia among frontline nurses.
Methods: A total of 1766 frontline nurses completed a self-reported online questionnaire by convenience sampling. The survey included six main sections: the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder (GAD-7), the 7-item Insomnia Severity Index (ISI), the 10-item Perceived Stress Scale (PSS-10), sociodemographic information, and work information. Multiple logistic regression analyses were applied to identify the potential significantly associated factors for psychological issues. The study methods were compliant with the STROBE checklist.
Results: 90.83% of frontline nurses were infected with COVID-19, and 33.64% had to work while infected COVID-19. The overall prevalence of depressive symptoms, anxiety and insomnia among frontline nurses was 69.20%, 62.51%, and 76.78%, respectively. Multiple logistic analyses revealed that job satisfaction, attitude toward the current pandemic management, and perceived stress were associated with depressive symptoms, anxiety, and insomnia.
Conclusions: This study highlighted that frontline nurses were suffering from varying degrees of depressive symptoms, anxiety, and insomnia during full liberalization of COVID-19. Early detection of mental health issues and preventive and promotive interventions should be implemented according to the associated factors to prevent a more serious psychological impact on frontline nurses.
Keywords: anxiety; depression; full liberalization of COVID-19; insomnia; nurses.
Copyright © 2023 Xiao, Liu, Peng, Wen, Lv, Liang, Fan, Chen, Chen, Hu, Peng, Wang and Luo.