Translating Research to Practice: Taking the Next Step to get Children Diagnosed with Cancer Moving
Bibliographic record
Abstract
Most research-based physical activity (PA) interventions show that children diagnosed with cancer experience healthrelated benefits during the intervention period.However, translating these interventions into practice is uncommon.To better understand if/how researchers translate their PA interventions to practice, we identified 65 researchers who had published research manuscripts/conference abstracts detailing PA interventions for children with cancer.Most authors reported their PA intervention was not translated into practice due to financing constraints and low adherence rates during the study period.Of those who did translate, strategies to overcome commonly cited barriers were provided.We can conclude that PA interventions are rarely translated to practice, as doing so is resource-intensive and requires concerted efforts from multiple stakeholders.Findings underscore the complicated nature of knowledge translation and raise questions about whose responsibility it is to move evidence to practice.
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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 | 0.203 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".