Exercise is Medicine Canada physical activity counselling and exercise prescription training improves counselling, prescription, and referral practices among physicians across Canada
Bibliographic record
Abstract
Exercise is Medicine Canada (EIMC) is an initiative that promotes physical activity (PA) counselling and exercise prescription within health care. The purpose of this study was to compare physicians' perceptions and practices around PA counselling and exercise prescription following EIMC training. Physicians (n = 46) from 7 different provinces completed questionnaires initially and 3 months following an EIMC workshop. Three months after intervention, physicians reported greater confidence compared with baseline for providing physical activity and exercise (PAE) information to patients (79% vs 55%; p < 0.001), assessing patients' PAE (69% vs 44%, p = 0.005), answering patients' PAE questions (78% vs 54%, p < 0.001), providing PAE advice (71% vs 43%, p < 0.001), and identifying which patients would benefit from referral to qualified exercise professionals (77% vs 52%, p = 0.002). At follow-up, physicians reported PA prescription barriers as less impactful (out of 4; all p < 0.05), including perceived patients' lack of interest (2.75 to 2.25), lack of available resources (2.59 to 2.00), and lack of time (2.41 to 2.14). The proportion of physicians providing written exercise prescriptions increased from 20% to 74%. This study suggests that the completion of a 1-day EIMC workshop increases physicians' confidence, knowledge, and counselling behaviours of physicians in prescribing PAE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".