Predicting the Quality of Sleep Based on Job Burnout and the Anxiety of Being Infected by the Coronavirus among the Treatment Staff Taking Care of Patients with COVID-19

Abstract

Background and Objective: Psychological problems including sleeping problems, anxiety, and job burnout are more prevalent among the treatment staff of the patients who suffer from coronavirus disease 2019 (COVID-19) rather than other treatment staff. Therefore, the current study aimed to predict the quality of sleep based on job burnout and the anxiety of being infected by the disease among this staff.
 Materials and Methods: The current study was a cross-sectional research in which 215 participants from the treatment staff filled out the relevant questionnaires online. The sampling was performed by available method and the instruments included Pittsburgh Sleep Quality Index (PSQI), Corona Disease Anxiety Scale (CDAS), and Maslach Burnout Inventory (MBI). Finally, the data were analyzed in two sections of descriptive and inferential statistics using SPSS software.
 Results: A correlation among all variables was observed. Moreover, the total model was significant (adjusted r² = 0.37, P = 0.01) and COVID-19 anxiety (standardized beta = 0.33, P = 0.01) and emotional exhaustion (standardized beta = 0.40, P = 0.01) predicted the sleep quality more than variables of depersonalization (standardized beta = -0.22, P = 0.01) and feeling of success (standardized beta = -0.12, P = 0.06).
 Conclusion: COVID-19 anxiety and job burnout are good predictors for sleep problems in the treatment staff of the patients who suffer from COVID-19.