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
<ns3:p> 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 Risk factors that significantly impacted the mental health of 1 <ns3:sup>st</ns3:sup> and 3 <ns3:sup>rd</ns3:sup> 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. </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".