Artificial neural network-based model for sleep quality prediction for frontline medical staff during major medical assistance.

ABSTRACT: Background: The sleep quality of medical staff was severely affected during COVID-19, but the factors influencing the sleep quality of frontline staff involved in medical assistance remained unclear, and screening tools for their sleep quality were lacking. Methods: From June 25 to July 14, 2022, we conducted an Internet-based cross-sectional survey. The Pittsburgh Sleep Quality Index (PSQI), a self-designed general information questionnaire, and a questionnaire regarding the factors influencing sleep quality were combined to understand the sleep quality of frontline medical staff in Fujian Province supporting Shanghai in the past month. A chi-square test was used to compare participant characteristics, and multivariate unconditional logistic regression analysis was used to d

SUBMITTER: Chen Q 

PROVIDER: S-EPMC11467980 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature