Moving from classroom to clinic: evaluating academic preparation for clinical exercise physiologists in Canada
Bibliographic record
Abstract
Clinical exercise physiologists (CEPs) play an essential role in delivering exercise-based interventions for individuals with chronic disease. While Canada's primary CEP certification, governed by the Canadian Society for Exercise Physiology (CSEP), outlines core competencies, the absence of program-level accreditation may lead to variability in academic preparation. This environmental scan evaluated the extent to which a subset of Canadian undergraduate programs align with CSEP-CEP certification requirements. Thirteen programs that participate in CSEP's Recommended Course Map initiative were examined from among 50+ kinesiology and exercise science programs operating nationally. Curricula were reviewed using structured coding of course content, skill assessment practices, and practicum integration. All programs demonstrated strong coverage of foundational knowledge; however, inconsistencies were observed in clinically-focused domains (e.g., health behaviour change, pharmacology), structured skill assessment, and supervised practicum experiences. Only 69% of programs included formal in-curriculum applied skill evaluations that directly address CSEP-CEP competency requirements, and 54% required a for-credit practicum. Practicum hours and settings varied widely, often falling short of national and international benchmarks. Findings highlight the need for stronger curriculum alignment, particularly in applied competencies and experiential learning. These results suggest that integrating structured, competency-based instruction and assessment across coursework and clinical placements would strengthen graduate readiness for both certification and clinical practice in healthcare contexts. This study provides timely insights to inform national CEP education standards and support strategic workforce planning as provinces expand CEP integration into healthcare systems.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".