An Integrated Knowledge Translation Approach to Developing A Story-Based Positive Youth Development Program in Sport
Bibliographic record
Abstract
Despite the established physical, social, and emotional benefits of participating in youth sport, such outcomes are not guaranteed. Indeed, purposeful efforts must be made to ensure that sport offerings are age-appropriate, promote engagement and enjoyment, and involve quality social relationships (e.g., Côté et al., 2020). The current article describes an integrated knowledge translation (iKT) partnership that developed a free story-based positive youth development (PYD) program for young ice hockey players (aged 10 to 12 years) in North America. The aim of the ‘1616 Program’ is to use elite ice hockey players as role models—through storytelling—to serve as motivating agents to introduce and engage young athletes with important concepts pertaining to PYD. Content from the general and sport-specific PYD literature (e.g., Côté et al., 2010; Lerner, 2006) informed decisions during program development, with the process generally being guided by the Knowledge-To-Action (KTA) framework (Graham et al., 2006). Herein, we describe the iKT collaborative process that could serve as a template for other researchers interested in partnering with relevant invested partners to create youth development programs.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".