Optimizing PYD in sport programs: Examining associations between program characteristics and developmental experiences
Bibliographic record
Abstract
Among growing societal concern for youth's healthy development, it has been proposed that sport programs can serve as contexts to foster healthy psychosocial development and life skills (Fraser-Thomas et al., 2005). Despite this, little research has focused on how different types of sporting programs may be facilitating positive experiences and outcomes among youth. The purpose of this study was to examine associations between program characteristics and youths' developmental experiences within these programs. Two hundred fourteen youth aged 10-18 involved in a diverse range of programs completed the Youth Experience Survey for Sport (YES-S; MacDonald et al., 2009). Results indicate sport type (i.e., team/individual), competition level (i.e., recreational/competitive), coach characteristics (i.e., age, gender) and contextual factors (i.e., number of coaches, group size) are associated with significantly different experiences in the areas of initiative, goal setting, cognitive skills, and negative experiences. Findings suggest further exploration is necessary to fully understand the processes and mechanisms that may be contributing to more positive or negative experiences in youth sport programs. Discussion will focus on how findings can begin to inform future guidelines and reform strategies in youth sport programs. Acknowledgments: SSHRC - SCRI (Sport Canada Research Initiative)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".