Investigating Youth Sport Coach Perspectives of an Asthma Education Module
Bibliographic record
Abstract
Physical activity can reduce symptoms and improve wellbeing in people who have asthma, and organized sport is one way for children and youth with asthma to engage in exercise. While asthmatic youth may experience a number of barriers to sport participation, healthy physical and social sport environments supported by coaches can help asthmatic youth athletes maintain long-term engagement in activity. This paper reports results of an assessment of an online coach education tool related to air quality, physical activity, and allergic disease (e.g., asthma). Focus groups with youth team sport coaches in southern Ontario ( n=12 participants) were conducted to explore how users experience the module and short- and medium-term outcomes of implementation. Although coaches perceive the module as relevant, it is considered less valuable in certain contexts (e.g., indoor environments) or when compared with other coach education (e.g., tactical). Although broad asthma management behaviours (e.g., athlete medical forms) were recognized, specific module-identified prevention and management techniques (e.g., the Air Quality Health Index) were less frequently described. Ensuring environment and health coach education emphasizes athlete performance while reducing risk is critical to promoting module application and providing safe and enjoyable youth team sport spaces.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".