Coaching athletes with an intellectual disability virtually: A participant-driven approach to developing an online coach training resource
Bibliographic record
Abstract
The COVID-19 pandemic forced a dramatic stop or pivot for sport organizations globally. To remain connected with their members, sport organizations, such as Special Olympics, began providing virtual team sessions and drop-in exercise classes for its athletes. However, many coaches or instructors have little to no experience conducting such virtual sport or exercise sessions, or use online conferencing systems. Thus, this project aims to provide an educational resource for coaches to effectively connect and coach their athletes in a virtual space. To ensure that the resource meets the needs of coaches, athletes, and administrators while including relevant academic literature, a set of advisory boards were established and will be referred to as a group, Advisors. The Advisors are guiding this three-phased project with monthly meetings to review materials (including summarized data) and collaborate on next steps. Phase 1 is a set of focus groups with coaches across Canada that have experience working with athletes with an intellectual disability. This phase is the focus of the presentation. Coaches were asked about their experience working with athletes with an intellectual disability pre- and during COVID-19, the resources they have available to them, the resources they wish they had, and the challenges that they face (or did face). Using an interpretive approach to the thematic analysis, results are categorized as Content, Delivery Style, Accessibility Features, and Challenges Beyond a Coach's Control. The results will inform the ongoing Phases 2 and 3 of this project which are to develop and evaluate the resource with coaches across Special Olympics Canada.
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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 | 0.048 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".