Individual and contextual predictors of retention in Special Olympics for youth with intellectual disability: who stays involved?
Bibliographic record
Abstract
BACKGROUND: Despite the recognised benefits of sport, participation is often reported to be low for youth with intellectual disability (ID). The current study was the first to longitudinally examine sport retention in this population, a critical aspect of ensuring participation. METHODS: Study participants were parents/caregivers of athletes with ID involved in community Special Olympics (SO), 11-22 years of age (N = 345). Participants completed an online survey in 2012 that included caregiver demographic and athlete intrapersonal, interpersonal and broader contextual variables. Retention rates for 2019 were determined using the SO provincial registration lists. RESULTS: Of the 345 survey participants, 81.7% remained active athletes in 2019. Caregiver demographic and athlete intrapersonal factors were largely unrelated to retention. In contrast, retention was associated with the frequency and number of sports athletes participated in, the perceived psychosocial gains of SO involvement and the environmental supports that were available to facilitate participation; frequency of sport participation was the strongest predictor of remaining a registered athlete. CONCLUSIONS: This study has implications for future initiatives aimed at increasing sport retention in a population that struggles to be engaged in sport. Efforts should focus on the athlete experience and sport-specific factors. Coaches and caregivers can foster positive experiences and play an important role in continued sport participation.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".