Do physical activity intensity and sedentary behaviour relate to burnout among medical students? Insight from two Canadian medical schools
Bibliographic record
Abstract
Background: Medical school involves high expectations of medical students, which may increase their risk for burnout. Physical activity (PA) and sedentary behaviour (SB) are modifiable risk factors for burnout. However, medical students are insufficiently taught about PA and SB and may therefore be less likely to meet guideline-recommended levels of these two movement behaviours or promote them in practice. Few studies have examined the relationships between medical students' PA intensity, SB, and burnout; such examination could help clarify educational needs for improving levels of movement behaviours and their promotion. Purpose: This study investigated (1) the relationships between light, moderate, vigorous, and total PA, SB, and burnout among medical students, and (2) moderate-to-vigorous PA as a moderator of the relationship between SB and burnout, to guide future curriculum renewal. Methods: = 129) at two Canadian institutions completed online validated questionnaires assessing light, moderate, vigorous, and total PA, SB, and burnout. Results: = .013) were negatively associated with burnout. Moderate-to-vigorous PA did not significantly moderate the relationship between SB and burnout. Conclusions: Engaging in lighter forms of PA and SB within guideline recommendations may help mitigate medical student burnout. Competencies to promote movement behaviours may dually target medical student burnout and curriculum gaps.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".