Opposition and opportunity: Reported challenges and changes to practice within the context of the exercise is medicine canada initiative
Bibliographic record
Abstract
Exercise is Medicine Canada (EIMC) is a national initiative aimed at increasing the number of health care providers (HCPs) assessing, counseling and prescribing exercise as part of routine health care visits. The purpose of this paper is to examine HCP's biggest perceived challenges in promoting exercise, how they intended to overcome these challenges, and to determine whether physicians followed through on proposed changes to their practice following an EIMC training workshop. As part of a larger EIMC evaluation, data from 181 HCPs (52% physicians) was drawn from questionnaires administered at training workshops. This data generated 249 challenge-statements and 75 statements about how HCPs planned to overcome these challenges. The most common challenges reported were patients' lack of interest (28% of total responses), lack of time (18%) and HCPs' lack of knowledge (12%). To overcome these challenges, HCPs reported that they would discuss exercise with more patients (55%), and increase use of exercise-related resources (32%). Data from 47 physicians generated 93 statements regarding proposed changes to practice immediately following the workshop. Primary responses included actively prescribe specific exercises (27%) and discuss in more depth (26%). At follow-up 2-3 months later, 88 statements were generated, with 46% reflective of at least one of the proposed changes, and 40% reflecting changes that were different than originally proposed. These findings suggest that despite challenges HCPs are looking to overcome these hurdles, and that the changes made reflect both the training received, as well as ways in which physicians are managing their clinical reality.Acknowledgments: Supported by the Lawson Foundation
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".