Wellbeing and mental health amongst medical students in Canada
Bibliographic record
Abstract
RESEARCH: There is abundant data revealing that there is significant rate of rates of Psychiatric morbidity, psychological stress, and burnout in the medical student population. A core study group in the UK collaborated with 12 countries around the world to review medical student wellness. In this context we surveyed 101 medical students at the Cummings medical school, Calgary, Canada during the height of the COVID pandemic regarding their wellbeing and mental health. RESULTS/MAIN FINDINGS: Prior to medical school 27% reported a diagnosis with a mental disorder. Whilst at medical school 21% reported a mental health condition, most commonly an anxiety disorder and or depressive disorder. The most commonly reported source of stress was study at 81%, the second being relationships at 62%, money stress was a significant source of stress for 35%, and finally 10% reported accommodation or housing as stressful. Interestingly only 14% tested CAGE positive but 20% of students reported having taken a non-prescription substance to feel better or regulate their mood. Seventy-five percent of medical students met specific case criteria for exhaustion on the Oldenburg Burnout inventory 74% met criteria for the GHQ questionnaire. CONCLUSIONS: These findings confirm that medical students are facing significant stressors during their training. These stressors include, in order of frequency, study, relational, financial, and accommodation issues. Nonprescription Substance use was a common finding as well as exhaustion and psychiatric morbidity. Future interventions pursued will have to address cultural issues as well as the organizational and individual determinates of stress.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".