Exercise in medicine: challenges and opportunities integrating qualified exercise professionals (QEPs) into Canadian healthcare
Bibliographic record
Abstract
Despite growing evidence on the role of physical activity in preventing and managing chronic disease, integration of qualified exercise professionals (QEPs) into the Canadian healthcare system remains limited and inconsistent. We explore the systemic and professional challenges hindering QEP integration, based on findings from a national initiative led by Exercise is Medicine Canada. Consultation occurred with numerous stakeholders across Canada and 10 leaders in health and exercise professions were interviewed. Using thematic analysis, four key barriers were identified: (1) overlapping and unclear scopes of practice; (2) insufficient clinical training and experiential education; (3) limited public and provider understanding of QEP roles; and (4) a reactive healthcare system that undervalues prevention. Participants emphasized the need for standardized, competency-based education and credentialling pathways aligned with clinical expectations, as well as improved communication of the distinct value QEPs offer in chronic disease management and health promotion. These findings highlight opportunities for coordinated action among academic institutions, professional bodies, and healthcare policymakers to advance the integration of QEPs and better support physical activity and exercise as a cornerstone of healthcare.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".