Medical students’ perspectives on a longitudinal wellness curriculum: a qualitative investigation
Bibliographic record
Abstract
Introduction: There is growing concern about the mental health status of medical students. Medical students are at a higher risk for depression, anxiety, and burnout than non-medical students. The Undergraduate Medical Education (UGME) Office of Medical Learner Affairs at McGill University developed a Longitudinal Wellness Curriculum (LWC) to foster medical students' well-being, self-care, and adaptability. Methods: We conducted a qualitative descriptive study to explore students' experiences with the LWC. We conducted three semi-structured focus groups involving a total of 11 medical students. We used thematic framework analysis for data analysis. Results: We found four main themes related to participants' engagement with the curriculum: 1) diverse perceptions on curriculum relevance and helpfulness; 2) the benefits of experiential sessions, role model speakers, and supportive staff; 3) insights on student-friendly curriculum scheduling; and 4) the importance of wellness education and systemic interventions in medical education. Conclusions: Most participants found the curriculum valuable and supported its integration into the academic curriculum. Experiential and active learning, diverse approaches to wellness, small group sessions, role modeling, and student-centered approaches were preferred methods. Inconvenient curriculum scheduling and skepticism over system-level support were seen as barriers to curriculum engagement and uptake. The findings of our study contribute to the development and implementation of wellness curriculum efforts in medical education.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".