Interventions to improve the well-being of medical learners in Canada: a scoping review
Bibliographic record
Abstract
Background: Medical education affects learner well-being. We explored the breadth and depth of interventions to improve the well-being of medical learners in Canada. Methods: We searched MEDLINE, EMBASE, CINAHL and PsycINFO from inception to July 11, 2020, using the Arksey–O’Malley, 5-stage, scoping review method. We included interventions to improve well-being across 5 wellness domains (i.e., social, mental, physical, intellectual, occupational) for medical learners in Canada, grouped as undergraduate or graduate nonmedical (i.e., health sciences) students, undergraduate medical students or postgraduate medical students (i.e., residents). We categorized interventions as targeting the individual (learner), program (i.e., in which learners are enrolled) or system (i.e., higher education or health care) levels. Results: Of 1753 studies identified, we included 65 interventions that aimed to improve well-being in 10 202 medical learners, published from 1972 through 2020; 52 (80%) were uncontrolled trials. The median year for intervention implementation was 2010 (range 1971–2018) and the median length was 3 months (range 1 h–48 mo). Most (n = 34, 52%) interventions were implemented with undergraduate medical students. Two interventions included only undergraduate, nonmedical students; none included graduate nonmedical students. Most studies (n = 51, 78%) targeted intellectual well-being, followed by occupational (n = 32, 49%) and social (n = 17, 26%) well-being. Among 19 interventions implemented for individuals, 14 (74%) were for medical students; of the 27 program-level interventions, 17 (63%) were for resident physicians. Most (n = 58, 89%) interventions reported positive well-being outcomes. Interpretation: Many Canadian medical schools address intellectual, occupational and social well-being by targeting interventions at medical learners. Important emphasis on the mental and physical well-being of medical learners in Canada warrants further exploration.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".