Identifying Mental Health Risk in Medical Students: A Quantitative Analysis of Wellness Assessments for First- and Third-Year Students at the University of Ottawa
Bibliographic record
Abstract
Introduction: An alarming prevalence of burnout among medical students has been reported in many countries, including Canada. To design resilience and wellness programs, it is important to explore individual risk factors. This article presents an example of a Wellness Assessment Program for medical students at the University of Ottawa. The goal was to identify risk factors for poorer mental health outcomes among medical students at the University of Ottawa. Methods: We conducted a quantitative study using a Wellness Assessment Questionnaire among four cohorts of first- and third-year students. Results: year medical students at the University of Ottawa were physical health, sleep/fatigue, social support, education and career, stress, and drug and/or alcohol use. Students who were originally from Ottawa had more social support and less stress and drug and/or alcohol use (p < 0.001;p=0.009). Being in the Francophone cohort had a positive effect on physical health, but a negative effect on psychological/emotional health (p=0.039;p=0.004). There was a statistically significant difference (p=0.021) between the psychological/emotional health of 1st-year students (M=0.7895) and 3rd-year students (M=0.8923) when covariates (risk factors) were not considered. Discussion: In the current context of the limited effectiveness of measures to address the negative impacts of medical education on student well-being, this study showed that efficient use of the Wellness Assessment Program data can identify risk factors that have a significant impact on wellness.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".