Description of a Community-Based Exercise Program for Children With Cancer: A Sustainable, Safe, and Feasible Model
Bibliographic record
Abstract
Background: Physical activity has emerged as a promising intervention to decrease the severity of cancer side effects. To date, only a few community-based exercise programs have been described in the literature. Of these, none have been designed to be sustainable and available as programs for pediatric cancer survivors on an ongoing basis. Methods: This article aims to describe a safe, feasible, and sustainable community-based exercise program for children with cancer. The program is offered to children on/off treatment and their siblings, between 3 and 18 years old. A multidisciplinary team developed this evidence-based program, and it is facilitated by trained volunteers. A parent survey was conducted to evaluate the quality of the program. Results: The PEER (Pediatric cancer patients and survivors Engaging in Exercise for Recovery) program is a safe, feasible, and sustainable community-based exercise program for children with cancer. From the parent satisfaction survey, all of the parents would strongly recommend the PEER program to other families. Conclusion: PEER provides an example of a community-based exercise program that has a strong pedagogical focus, is evidence-based, and is individualized, safe, feasible, and sustainable for children with cancer. On the basis of the benefit of exercise described in the literature, we believe this model of an evidence-based community intervention might decrease the burden of cancer side effects and promote the reintegration of children affected by cancer into physical activity programming in their community.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".