Connecting People with Cancer to Physical Activity and Exercise Programs: A Pathway to Create Accessibility and Engagement
Bibliographic record
Abstract
Recent guidelines concerning exercise for people with cancer provide evidence-based direction for exercise assessment and prescription for clinicians and their patients. Although the guidelines promote exercise integration into clinical care for people with cancer, they do not support strategies for bridging the guidelines with related resources or programs. Exercise program accessibility remains a challenge in implementing the guidelines, but that challenge might be mitigated with conceptual frameworks ("pathways") that connect patients with exercise-related resources. In the present paper, we describe a pathway model and related resources that were developed by an expert panel of practitioners and researchers in the field of exercise and rehabilitation in oncology and that support the transition from health care practitioner to exercise programs or services for people with cancer. The model acknowledges the nuanced distinctions between research and exercise programming, as well as physical activity promotion, that, depending on the available programming in the local community or region, might influence practitioner use. Furthermore, the pathway identifies and provides examples of processes for referral, screening, medical clearance, and programming for people after a cancer diagnosis. The pathway supports the implementation of exercise guidelines and should serve as a model of enhanced care delivery to increase the health and well-being of people with cancer.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".