Practical Considerations for Coaching Athletes With Learning Disabilities and Neurodevelopmental Disorders
Bibliographic record
Abstract
Learning disabilities and neurodevelopmental disorders are the most prevalent disabilities that affect learning. This paper will provide practical recommendations and observations for coaching athletes with three common learning disabilities (dyslexia, dysgraphia, and dyscalculia) and two neurodevelopmental disorders (attention deficit hyperactivity disorder and autism spectrum disorder). Adapted from the literature and in conjunction with previous experiences, the authors provided a range of recommendations for coaches to consider implementing within their practices. The recommendations place an emphasis on the knowledge, strategies, and behaviors of the coach and their role in providing an inclusive, safe, and accessible space for athletes—with or without disabilities—rather than problematizing the disability or the person. Coaches are encouraged to consider their coaching environment (i.e., structure, physical elements, equipment), communication styles (i.e., language, delivery, feedback), and behaviors (e.g., frequent check-ins, review of material). Furthermore, coaches are encouraged to critically reflect on their preconceived biases, assumptions, and experiences with disability and how these play a role in influencing their coaching practices.Considering the prevalence of people with learning disabilities or neurodevelopmental disorders, it is essential for coaches to have access to disability-specific information while remaining cognizant of the needs of the individual when providing an inclusive environment for all.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".