Prescriptive Medicine: The Importance of Preparing Canadian Medical Students to Counsel Patients Toward Physical Activity
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) is powerful for preventing and treating many chronic diseases. Physicians' own PA behaviors are correlated with their likelihood to counsel patients regarding PA. Medical students' PA-related attitudes and behaviors reflect what can be expected from our future physicians. METHODS: A 27-item online survey was used to determine the percentage of Canadian medical students meeting the Canadian physical activity recommendations, and their self-reported perception of relevance and frequency of exercise counseling during patient encounters. We generated cross-tabulations with the independent covariates and our statistical comparison was based on the generalized estimating equation (GEE) algorithm to adjust for schools (clusters). RESULTS: While 64% (969/1510) of medical students met the MVPA recommendation, only 25% discussed PA counseling with patients. Most (80% and 90%, respectfully) believed physicians should adhere to a healthy lifestyle to effectively encourage their patients to do so, and that their credibility increased if they stayed fit themselves. CONCLUSIONS: Medical students are interested in and receptive to the importance of PA. However, not only is there improvement needed for the more than one-third of medical students who are insufficiently active themselves, but substantial change is needed regarding the vast majority of students' current counseling behaviors.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".