Health care provider confidence and exercise prescription practices of Exercise is Medicine Canada workshop attendees
Bibliographic record
Abstract
The Exercise is Medicine Canada (EIMC) initiative promotes physical activity counselling and exercise prescription within health care. The purpose of this study was to evaluate perceptions and practices around physical activity counselling and exercise prescription in health care professionals before and after EIMC training. Prior to and directly following EIMC workshops, 209 participants (physicians (n = 113); allied health professionals (AHPs) (n = 54), including primarily nurses (n = 36) and others; and exercise professionals (EPs) (n = 23), including kinesiologists (n = 16), physiotherapists (n = 5), and personal trainers (n = 2)) from 7 provinces completed self-reflection questionnaires. Compared with AHPs, physicians saw more patients (78% > 15 patients/day vs 93% < 15 patients/day; p < 0.001) and reported lower frequencies of exercise counselling during routine client encounters (48% vs 72% in most sessions; p < 0.001). EPs had higher confidence providing physical activity information (92 ± 11%) compared with both physicians (52 ± 25%; p < 0.001) and AHPs (56 ± 24%; p < 0.001). Physicians indicated that they experienced greater difficulty including physical activity and exercise counselling into sessions (2.74 ± 0.71, out of 5) compared with AHPs (2.17 ± 0.94; p = 0.001) and EPs (1.43 ± 0.66; p < 0.001). Physicians rated the most impactful barriers to exercise prescription as lack of patient interest (2.77 ± 0.85 out of 4), resources (2.65 ± 0.82 out of 4), and time (2.62 ± 0.71 out of 4). The majority of physicians (85%) provided a written prescription for exercise in <10% of appointments. Following the workshop, 87% of physician attendees proposed at least one change to practice; 47% intended on changing their practice by prescribing exercise routinely, and 33% planned on increasing physical activity and exercise counselling, measured through open-ended responses.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".