Examining the Youth Multi-Sport Event Environment: Implications towards athlete development and transitioning
Bibliographic record
Abstract
Many factors are associated with a person’s attitude formation and intention towards a behavior. In this study, we examined organizational factors that helped form young athletes attitudes regarding their future plans in high performance sport. Through a mixed method survey design, data was collected from 207 young pre-elite athletes who competed during the 2017 Commonwealth Youth Games in Bahamas. Several organizationally controlled aspects of the games environment were found to contribute to young athlete’s satisfaction with the event including their accommodations, available information regarding their sport and finally, the social and cultural activities during games-time. However, satisfaction with the games environment was not predictive of young athletes future intentions to remain in sport. Qualitative thematic coding denoted two key themes related to athlete plans to continue in high performance sport: level of satisfaction and learning. Further, qualitative results revealed five main impediments to continuing in high performance sport specifically, physiological, psychological, performance, environment and life concerns. The paper contributes to our understanding of the controllable and uncontrollable social and environmental factors in a multi-sport international event. Nationally controlled factors that influence young athletes attitude formation, specifically their satisfaction and intentions to remain in sport.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".