PYDSportNET: A knowledge translation project bridging gaps between research and practice in youth sport
Bibliographic record
Abstract
Generating a common understanding of knowledge translation among stakeholders is a key issue for increasing the use of research evidence in practice. The purpose of this article is to create a better understanding of knowledge translation in youth sport by providing a framework and guidelines for facilitating knowledge translation. We present PYDSportNET, a knowledge translation project designed to enhance the use of research evidence to promote positive youth development (PYD) through sport. This project is guided by the Knowledge to Action framework, which has two components (knowledge creation and the action cycle). For the knowledge creation component, we completed a meta-synthesis and created knowledge products. For the action cycle, we completed two studies with key sport stakeholders. Simultaneously, we created a knowledge dissemination and exchange network. Having described these activities, we go on to highlight some lessons learned to date and next steps for the project.
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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 | 0.082 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".