Balancing mental health through predictive modeling for healthcare workers during public health crises.

ABSTRACT: During public health emergencies such as SARS, Ebola, and COVID-19, healthcare workers (HCWs) are often on the front lines, placing them at increased risk for adverse mental health outcomes, particularly depression and anxiety. Despite this risk, there remains a scarcity of research focused on developing predictive models to forecast the depression and anxiety levels of healthcare workers under challenging conditions. A total of 349 HCWs were selected from a Tertiary Grade-A hospital in the city of Shenyang, Liaoning Province in China. Depression and anxiety were assessed using the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) scale, respectively. This study employed a random forest classifier (RFC) to predict depression and anxiety levels of HCWs from t

SUBMITTER: Wang J 

PROVIDER: S-EPMC12328615 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature