CanWell: meeting the psychosocial and exercise needs of cancer survivors by translating evidence into practice
Bibliographic record
Abstract
BACKGROUND: As more evidence emerges to support the incorporation of exercise for cancer survivors to positively affect physical, emotional, and social health, it is imperative that health-care providers use current knowledge to develop evidence-based exercise programs for these patients. Our purpose is to describe the development, implementation, and effectiveness of the CanWell program, an evidence-based, community and partnership-based, exercise, and education program for all people with cancer. METHODS: Exercise and cancer research was reviewed, summarized, and utilized to develop CanWell. A 12-week, supervised, community-based, exercise, and education program established in collaboration between an acute care hospital, academic center, and a not-for-profit YMCA facility. CanWell participants completed physical and health-related quality of life measures prior to initiating the program and repeated them at 6 and 12 weeks. RESULTS: Following the exercise program, participants reported significant improvements in health-related quality of life, recorded distance ambulated during a 6-min walk test, and total minutes on a treadmill recorded using the standardized exponential exercise protocol treadmill test. Furthermore, no increases in disease burden were identified using the Edmonton Symptom Assessment System. In addition, no exercise related injuries were reported by CanWell participants. CONCLUSIONS: As the body of evidence supporting the incorporation of exercise as a standard of care for cancer survivors, it is imperative that care providers use current knowledge to provide opportunities for their patients to exercise in effective exercise programs. CanWell is an example on how collaboration between hospital, university, and community institutions can be used to move research into practice and meet the needs of cancer survivors.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".