Understanding sources of knowledge for coaches of athletes with intellectual disabilities
Bibliographic record
Abstract
Coaching is a multidimensional concept that is influenced by a number of factors. Recent research has investigated the development of coaching knowledge (Erickson et al., 2008); however coach learning of athletes with intellectual disabilities is currently under researched. Therefore, the purpose of this study was to understand how coaches of athletes with intellectual disabilities gain their knowledge and compare this with how they would ideally like to learn. Forty-five Special Olympics Canada coaches participated in two structured telephone interviews investigating actual and ideal sources of coaching knowledge. The mean age of the coaches was 51 years (range: 20 – 72 years) and they had an average of 12 years experience coaching (range: 1 – 27 years). Coaching knowledge was categorized across the dimensions of competition, organization, and training. Coaches also completed two online questionnaires that assessed coaching efficacy and coach-athlete relationships. Results demonstrate that across the three domains, coaches primarily learned by doing and by consulting with their coaching peers. However, information about ideal sources of coaching knowledge revealed that coaches would value more structured coaching courses, learning from mentors, and increased administrative support, in addition to learning on their own and from peers. Results suggest that a broader approach to education should be incorporated into coaching athletes with intellectual disabilities. Recommendations for achieving such goals will be provided.Acknowledgments: We would like to acknowledge the support of Special Olympics Canada for conducting this research.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".