How Special Olympics coaches learn: Actual sources of development for coaches of athletes with intellectual disabilities
Bibliographic record
Abstract
Special Olympics is an organization that facilitates involvement in organized sport for individuals with intellectual disabilities. Researchers (Dykens et al., 1998; Harada & Siperstein, 2009) have linked participation in Special Olympics sport programs with a number of positive outcomes such as increased health, physical proficiency, and psychosocial skills. Coaches have been identified as influential adults in the lives of children (Petitpas et al., 2005) and have the potential to significantly impact the development of athletes (Conroy & Coatsworth, 2007). However, the development of coaches within Special Olympics has largely been unexplored. Therefore, the purpose of this study was to investigate actual sources of knowledge used by Special Olympics coaches. Quantitative interviews (Erickson et al., 2008) were conducted with Special Olympics coaches to collect coach demographics and to understand actual sources of coaching knowledge gained across the spheres of competition, organization, and training. Results indicate that the three most utilized sources of knowledge are learning by doing, individual planning, and interacting with coaching peers. This suggests that coaches are not gaining significant coaching knowledge through the resources available to them such as coaching courses and clinics. Implications for Special Olympics programming and coach development will be discussed.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".