Multiple recommended health behaviors among medical students in Western Canada: a descriptive study of self-reported knowledge, adherence, barriers, and time use
Bibliographic record
Abstract
Background: General medical practitioners are responsible for promoting and prescribing lifestyle modification and serve as role models for healthy behaviors. We aimed to assess self-reported knowledge, adherence, barriers and time spent on all recommended health behaviors among medical students. Methods: A cross-sectional online survey of eight behavioral domains among undergraduate medical students in The University of British Columbia, Canada, was analysed using descriptive statistics and visual display. Results: Between March and April 2023, 137 medical students participated in the survey (74% female). Over 80% had knowledge of five health behavior recommendations, but lacked knowledge of specific dietary recommendations in particular. Over 60% reported meeting guideline-recommended levels for tobacco, weekly alcohol, daily alcohol (females only), and physical activity (males only). Large gaps existed between knowledge and adherence for physical activity, sleep, sedentariness, screen time, and dietary recommendations. Sex differences in knowledge and adherence to recommended health behaviors were identified. Time spent on wellness focused on sleep (47-49 h/week), diet (9.6 h/week), exercise (5.8 h/week), and hobbies (6.1 h/week). Forgetting recommendations (69% of females, 71% of males), and lack of time (72% of females, 52% of males) were principal barriers to knowledge and adherence. Conclusion: Most medical students in Western Canada reported not meeting multiple recommended health behaviors. Time was the largest barrier to adherence and free time was spent on sleep. Medical education may require protected time and dedicated content for health behaviors to ensure future physicians can be role models of health promotion for patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".