Infusing disability into coach education and development: a critical review and agenda for change
Bibliographic record
Abstract
Background: The training of coaches is considered central to sustaining and improving the quality of sports provision. In Parasport, coaches are recognised at the highest level of international sport policy as performing a central role in achieving important sporting and social outcomes related to disabled people. However, an emerging body of evidence suggests that formal coach education plays only a minor role in Parasport coaches’ development. To ensure equitable access and quality experiences and opportunities for disabled people in sport there is an ongoing challenge to theorise and implement the optimal structure for educating coaches. Purpose: The purpose of this paper is to address the central theme of disability and coach education reform in sport. The aim is to review critically the emerging literature on coach development in Parasport to provide some clarity and consensus on existing pathways and models for coach development, before outlining some potential ways forward for coach education. Discussion and Conclusions: Starting with a critical review of existing research on coaches’ learning and development in Parasport, we then examine potential approaches to coach education, providing examples from existing research in coaching and the wider field of education. This is followed by some modest suggestions for coach education reform as it pertains to the inclusion of disability.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".