Exploring how recreational cycling promotes the quality of life of children treated for cancer
Bibliographic record
Abstract
After completing treatment, childhood cancer survivors are at an increased risk for a lifetime of health problems, which can impair their quality of life (QOL). These issues emphasize the importance of identifying strategies that improve the QOL of childhood cancer survivors. There is consistent evidence that physical activity (PA) interventions can enhance QOL, however, few studies have focused on unstructured PA or investigated the underlying mechanisms. Understanding the mechanisms that explain how unstructured PA enhances childhood cancer survivors' QOL will further our knowledge and enable the refinement of sustainable PA interventions to promote optimal QOL. The objective of our longitudinal qualitative study was to explore how childhood cancer survivors' perceptions of QOL changed as a result of participating in unstructured recreational cycling for 3 months. We conducted semi-structured interviews with 4 childhood cancer survivors (Mage = 10.5 years; SD = 2.5) before they received a bicycle, and 4- and 8-weeks after. We analyzed the data using thematic analysis. Cycling enhanced participants' QOL over time by helping them: (a) Feel stronger and less tired, (b) Experience support from their social networks, and (c) Enhance feelings of self-efficacy and normalcy. Our study demonstrates that unstructured PA improves QOL in childhood cancer survivors. It also provides preliminary information about the mechanisms for how PA may promote these beneficial effects. If confirmed in larger studies, our findings suggest that PA interventions should explicitly aim to promote childhood cancer survivors' perceptions of their physical, psychological, and social functioning in order to optimize QOL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".