Strategies for Fostering a Quality Physical Activity-Based Mentoring Program for Female Youth: Lessons Learned and Future Directions
Bibliographic record
Abstract
The current case outlines practical strategies used by youth leaders to implement a female-only physical activity-based mentoring program. This program was selected as the case for the current paper as it scored the highest on program quality out of 26 different sport and physical activity-based youth programs within a larger project. The two program leaders were interviewed to understand what practical strategies they used to foster a high-quality program within this context. The leaders discussed how they: (a) focused on developing individualized relationships with youth, (b) balanced structure with flexibility to allow for youth voice, (c) intentionally integrated life skills, and (d) combined engaging activities with downtime to differentiate the program from school. This case provides a practical account of how front-line workers in youth mentoring programs, specifically within sport and physical activity contexts, can deliver a quality program. Reflection on areas for future work within the field of sport psychology, including ways to bridge the gap between research and practice and the need to develop communities of practice for youth programmers, are presented.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".