Academic-related stressors predict depressive symptoms in graduate students: A machine learning study

Background: Graduate students face higher depression rates worldwide, which were further exacerbated during the COVID-19 pandemic. This study employed a machine learning approach to predict depressive symptoms using academic-related stressors.

Methods: We surveyed students across four graduate programs at a Federal University in Brazil between October 15, 2021, and March 26, 2022, when most activities were restricted to taking place online due to the pandemic. Through an online self-reported screening, participants rated ten academic stressors and completed the Patient Health Questionnaire (PHQ-9). Machine learning analysis tested whether the stressors would predict depressive symptoms. Gender, age, and race and ethnicity were used as covariates in the predictive model.

Results: Participants (n=172), 67.4 % women, mean age: 28.0 (SD: 4.53) fully completed the online questionnaires. The machine learning approach, employing an epsilon-insensitive support vector regression (Ɛ-SVR) with a k-fold (k=5) cross-validation strategy, effectively predicted depressive symptoms (r=0.51; R²=0.26; NMSE=0.79; all p=0.001). Among the academic stressors, those that made the greatest contribution to the predictive model were "fear and worry about academic performance", "financial difficulties", "fear and worry about academic progress and plans", and "fear and worry about academic deadlines".

Conclusions: This study highlights the vulnerability of graduate students to depressive symptoms caused by academic-related stressors during the COVID-19 pandemic through an artificial intelligence methodology. These findings have the potential to guide policy development to create intervention programs and public health initiatives targeted towards graduate students.