Co-Producing the International Pediatric Oncology Exercise Guidelines (iPOEG) Toolkit with End-Users
Bibliographic record
Abstract
The international Pediatric Oncology Exercise Guidelines (iPOEG) support physical activity among children and adolescents affected by cancer. Knowledge translation efforts are needed to ensure that those who will use and/or benefit from the iPOEG have access to it. This mixed-methods study followed co-production principles as guided by an integrated knowledge translation approach within the Knowledge to Action (KTA) Framework, to engage end-users (i.e., professionals and patients/caregivers) to: (1) identify the types of resources needed; (2) co-produce an iPOEG brand; (3) co-produce resources and content; and (4) co-produce dissemination plans to distribute the iPOEG Toolkit (i.e., resources and content). End-users indicated requiring resources such as posters, infographics, social media posts, and videos, and co-created resource content covering quick tips to get active and movement-related education and information. Generated strategies to disseminate the iPOEG Toolkit included: (1) academic presentations; (2) brief education sessions and facilitated discussions to different end-user groups; (3) engaging champions from different end-user groups; (4) emails and email reminders; (5) mainstream news outlets (e.g., newspaper, magazines, or segments on the television [i.e., local news]); and (6) social media. Co-production of the iPOEG Toolkit and dissemination plan were guided by two phases within the KTA framework to build tools that can promote the reach of the iPOEG, ultimately increasing physical activity in this population.
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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 | 0.055 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".