Coaching Strategies Used to Deliver Quality Youth Sport Programming
Bibliographic record
Abstract
Coaches are primary influencers in helping youth achieve positive developmental outcomes in sport; however, it is not well understood how coaches achieve quality program delivery. The purpose of this study was two-fold: (a) to understand strategies that coaches used to facilitate program quality in youth sport and (b) explore differences in strategies between recreational and competitive programs. Twenty-five coaches participated in semistructured interviews, where they discussed strategies employed for program delivery. Interviews were guided, in-part, by Eccles and Gootman’s eight setting features that should be present within a program for youth to achieve positive developmental outcomes. An inductive-deductive thematic analysis was employed, in which strategies associated with facilitating program quality were interpreted inductively, and then categorised deductively under a relevant setting feature. Results indicated that coaches used unique strategies across all eight setting features, with a predominant focus on strategies to support youth’s efficacy and mattering (e.g., giving positive reinforcement) and opportunities for skill-building (e.g., valuing holistic development of youth), with lesser focus on strategies that involved integrating family, school, and community. Practical implications are discussed on how coaches can use strategies to address multiple setting features and recommendations are provided for improving program delivery.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".