Coaches’ Reflections of Using a Charity-Driven Framework to Foster Youth Athletes’ Psychosocial Outcomes
Bibliographic record
Abstract
When structured appropriately, sport can promote psychosocial development in youth athletes. However, few frameworks exist that allow coaches to intentionally support youth’s psychosocial development through their sport programming. The Play Better framework represents one intentional approach that incorporates prosocial behavior where youth earn donations toward charitable causes for reaching process-based goals. Given the potential benefit that explicit strategies have for yielding positive developmental outcomes, there is a need for research to explore the role of intentionality in enhancing quality sport delivery. The purpose of this study was to understand coaches’ perceptions of using the framework within their coaching practices. Twenty-three soccer coaches (83% male) participated in a one-on-one semistructured interview analyzed inductively. Results indicated that coaches perceived the Play Better framework to (a) help enact their coaching philosophies; (b) enable youth choice, while supporting sport-skill development and enjoyment; (c) facilitate intentional approaches to life skills development and transfer; and (d) foster professional and personal development. This research provides initial evidence of the benefit of using an intentional framework, like Play Better, for athletes and coaches. Future research is needed to understand athlete and parent perspectives of utilizing the framework. Findings help inform future coach training resources and best practices.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| 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".