Exploring the Experiences of Coaching Children with Autism Spectrum Disorder in Canadian Aquatic Programs
Bibliographic record
Abstract
Teaching children how to swim provides them with the necessary skills to maintain a physically active lifestyle. Many children with an Autism Spectrum Disorder (ASD) diagnosis have shown interest in participating in swimming lessons. Studies have demonstrated that teaching students with ASD how to swim has aided in their motor performance (Yanardag, Nurgil, & Akmanoglu, 2013), their ability to initiate social interactions with peers (Chu & Pan, 2012) and a reduction in stereotypical behaviours associated with ASD (Vonder Hulls, Walker, & Powell, 2006). In order to meet the needs of this unique group of students, swim instructors take part in a variety of training programs amongst other experiences to effectively prepare themselves to teach their students with ASD. This study consists of three individual case studies designed to examine the journeys that swim instructors have taken in order to effectively coach students with ASD how to swim, along with encouraging motor skill development, social interactions and reducing stereotypical behaviours. Two research questions guide this study: What are the experiences of swim instructors who work with children with ASD? What are the practical teaching strategies in swimming lessons that are effective for social skill development, reducing stereotypical behaviours and motor skill development in children with ASD? The findings of this study which focus on strategies of effective practice include: gestural communication, positive practice, contact communication, complexities of choice, and encouraging independence and confidence. Along with these findings, the instructors credit the following sources for their construction of knowledge: peer learning and mentorship, parental/caregiver support, and the value of certification and training. It is clear that instructors access a number of sources for their coaching development, but current swim instructor certification programs require revisions to increase instructor preparedness. This study contributes to current literature which focuses on coaching individuals with developmental disabilities. The findings may provide coaches with insight into strategies of effective practice for a more inclusive and equitable community.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".