Physical activity prescription by Canadian Emergency Medicine Physicians
Bibliographic record
Abstract
An increase in physical activity has been shown to improve outcomes in many diseases. An estimated 600 000 Canadians receive their primary health care from emergency departments (ED). This study aims to examine physical activity prescription by emergency medicine physicians (EPs) to determine factors that influence decisions to prescribe physical activity. A survey was distributed to EPs via email using the Canadian Association of Emergency Physicians (CAEP) survey distribution protocol. Responses from 20% (n = 332) of emergency physician/residents in Canada were analyzed. Of the EPs, 62.7% often/always counsel patients about preventative medicine (smoking, diet, and alcohol). Only 12.7% (42) often/always prescribe physical activity. The CCFP-trained physicians (College of Family Physicians Canada) were significantly more likely to feel comfortable than CCFP-EM-trained physicians (Family Physicians with Enhanced Skills in Emergency Medicine) prescribing physical activity (p = 0.0001). Both were significantly more likely than the FRCPC-trained EPs (Fellows of the Royal College of Physicians of Canada). Of the EPs, 73.4% (244) believe the ED environment does not allow adequate time for physical activity prescription. Family medicine-trained EPs are more likely to prescribe physical activity; the training they receive may better educate them compared with FRCPC-trained emergency medicine. Further education is required to standardize an approach to ED physical activity prescription.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".