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

作者：Jiana Wang, Lin Feng, Nana Meng, Cong Yang, Fanfan Cai, Xin Huang, Yihang Sun, Kristin K. Sznajder, Lu Zhang, Pin Yao · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-14403-3 · 被引用次数：2 · 研究领域：COVID-19 and Mental Health、Mental Health Treatment and Access、Workplace Health and Well-being

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 three perspectives: individual, interpersonal, and institutional with SHAP values to assess the contribution of factors. The Synthetic Minority Over-sampling Technique (SMOTE) was employed to address the issue of imbalanced data distribution. The prevalence of depression and anxiety among HCWs was 28.37% and 33.52%, respectively. The prediction model was developed using a training dataset (70%) and a test dataset (30%). The area under the curve (AUC) for depression and anxiety was 0.88 and 0.72, respectively. Additionally, the mean values of the 10-fold cross-validation results were 0.77 for the depression prediction model and 0.79 for the anxiety prediction model. For the depression predicti...