Exercise is medicine Canada workshop training improves physical activity practices of physicians across Canada, independent of initial confidence level
Bibliographic record
Abstract
Background: Educational workshops help physicians (MDs) include physical activity and exercise (PAE) content in more patient appointments. It is unclear if MDs with varying degrees of self-confidence discussing PAE with their patients equally benefit from such training. We evaluated whether MDs’ initial self-confidence affects the impact of an educational PAE workshop. Methods: MDs (n = 63) across Canada completed self-reflection questionnaires initially and 3-months following a PAE workshop. MDs were divided into low-confidence [self-efficacy score (out of 100%): <40%; n = 21], medium-confidence (40-60%; n = 19) and high-confidence (>60%; n = 23). Results: PAE counselling self-efficacy increased in all groups (relative increase: Low=~40%, Medium=~20%, High=~10%). Training increased the low-confidence group’s knowledge, awareness of guidance/resources and perception of their patients’ interest in lifestyle management (~30% change; all p < 0.001). Compared to baseline, a greater proportion (all p < 0.001) of MDs reported prescribing exercise at 3-month follow-up in each of the low-confidence (10% to 62%) medium-confidence (16% to 89%) and high-confidence (57% to 87%) groups. Conclusion: PAE training favorably improved MDs’ self-confidence, perceived impact of many barriers and the proportion of MDs prescribing exercise, at each level of confidence. An educational workshop particularly assisted MDs with low-confidence (i.e., those who needed it the most) integrate PAE into their practice.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".