University Students in Times of Crisis: Machine Learning-Driven Exploration of Individual and Social Determinants of Psychological Distress
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted the mental health of university students, exacerbating pre-existing vulnerabilities. This study investigates psychological distress within the Université du Québec network by examining individual and social determinants using both inferential statistics and machine learning approaches. A dataset comprising 4,691 students was analyzed using validated instruments, including the PHQ-4 for psychological distress, along with measures of spiritual well-being and social support. After preselecting key variables through bivariate analyses and controlling for multicollinearity, several predictive models were tested. Among them, CatBoost regression achieved the best performance (R2 = 0.5913, relative MAE \(= 11.90\%\)) in predicting PHQ-4 scores. SHapley Additive exPlanations (SHAP) were used to interpret the model’s predictions, identifying daily stress, mental health stability, and spiritual health as the most influential features. Results highlight the predominant role of modifiable, experience, based factors, such as stress perception and emotional resilience, over fixed demographic attributes. This dual-method approach offers valuable insights for designing targeted mental health interventions, especially in crisis contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".