Athletes with a physical disability: Perspectives on their coaching environment
Bibliographic record
Abstract
Research has shown that persons with a disability who participate in sport often develop a positive sense of self, a sense of personal empowerment, and a sense of connectedness to society (e.g. DePauw & Gavron, 2005; De Potter, 2006). Coaches of athletes of all ages, whether these athletes are able-bodied or athletes with a disability, play a vital role in facilitating such positive experiences (Hansen, Larson, & Dworkin, 2003; Coté & Sedgwick, 2003). A Canadian analysis of research priorities in disability sport noted the coaching area was in dire need of data-based research to assess the effectiveness of coaches' training programs and sport versus health coaching backgrounds (Reid & Prupas, 1998). The purpose of this presentation is to communicate the findings from Phase one of a three-phase research project examining the developmental learning processes of coaches of athletes with a physical disability. In this first phase, 17 athletes with a physical disability were interviewed to better understand their coaching environment. The findings indicated that the athletes felt their coaches were generally competent and encouraging. However, the athletes also discussed the need for their coaches to communicate with them regarding their abilities and special needs to effectively determine the best course of action for training and competition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".