Role of Qualified Exercise Professionals in Medical Clearance for Exercise: Alberta Cancer Exercise Hybrid Effectiveness-Implementation Study
Bibliographic record
Abstract
BACKGROUND: Current guidelines endorse the integration of exercise into cancer care. The diagnosis of cancer and its treatment, however, may introduce factors that make exercise engagement difficult, especially for individuals with advanced stages of disease. In this paper, we describe the baseline screening and triage process implemented for the Alberta Cancer Exercise (ACE) hybrid effectiveness-implementation study and share findings that highlight the multifaceted complexity of the process and the direct role of the clinical exercise physiologist (CEP). METHODS: ACE was a hybrid effectiveness-implementation study examining the benefit of 12-week cancer-specific community-based exercise program. The ACE screening process was developed by integrating evidence-based guidelines with oncology rehabilitation expertise to ensure safe and standardized participation across cancer populations. The screening process involved four steps: (1) a pre-screen for high-risk cancers, (2) completion of a cancer-specific intake form and the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+), (3) a CEP-led interview to further evaluate cancer status, cancer-related symptoms and other health issues (performed in-person or by phone), and (4) a baseline fitness assessment that included measurement of vital signs. RESULTS: A total of 2596 individuals registered and underwent prescreening for ACE with 2570 (86.6%) consenting to participate. After full screening including the baseline fitness testing, 209 participants (8.1%) were identified as requiring further medical clearance. Of these, 191 (91.4%) had either a high-risk cancer, metastatic disease or were in the palliative end-stage of cancer, and 161 (84.3%) reported cancer-related symptoms potentially affecting their ability to exercise. In total, 806 (31.4%) participants were triaged to CEP-supervised in-person programming, 1754 (68.2%) participants to ACE community programming, and 8 (0.3%) specifically to virtual programming (post-COVID-19 option). CONCLUSIONS: The findings highlight the complexity and challenges of the screening and triage process, and the value of a highly trained CEP-led iterative approach that included the application of clinical reasoning.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".