Mental health and wellness of medical students in Ontario – A mixed methods approach
Bibliographic record
Abstract
Medical students are highly susceptible to distress and burnout, raising concern about how this can carry over into their careers, impacting wellness, professionalism, and patient care. The aims of this study are to assess the mental health and wellness status of medical students in Ontario. This study electronically surveyed medical students across M1-M4 attending a medical school in Ontario, Canada, using the Brief COPE, DASS21, and Copenhagen Burnout Inventory (CBI) to examine their coping mechanisms, levels of stress, anxiety, and depression, the prevalence of personal, work-related, and patient-related burnout, and perceived support. Participants were recruited through mass email containing a survey link. BriefCOPE results show that students tend to rely on self-distraction, emotional support, instrumental support, and acceptance as coping mechanisms. DASS21 demonstrates that M4 students report severe stress and anxiety and mild depression. Results of CBI show moderate personal and work-related burnout, and low patient-related burnout. Qualitative analysis of students' opinion on how the institution can enhance their wellness resulted in three themes: enhanced student scheduling, more communication from the administration, and more elective wellness sessions and social events. The data presented in this study will provide insight into the overall mental health and wellness of medical students studying in Ontario and may have important implications for medical education, healthcare institutions, and health policymaking.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".