What Is a Parasport Coach’s Role During Athlete Classification? Exploring How Parasport Coaches Learn About Classification and Their Role Within This Process
Bibliographic record
Abstract
Undergoing classification can be a difficult experience for athletes with disabilities, yet coaches may support athletes during this event. However, research has yet to examine either coaches’ roles during classification or how coaches learn to navigate this unique aspect of parasport. We purposed to explore parasport coaches’ roles during classification as well as the ways in which coaches learn about classification. Twelve Canadian high-performance coaches representing eight parasports participated in semistructured interviews. Inductive reflexive thematic analysis of the transcripts was conducted. Results show coaches view their role as intuitive and centered on preparing the athlete, ensuring fairness, and reframing classification outcomes. The ways coaches learned about classification varied, but coaches agreed there is a general lack of structured resources available to coaches interested in learning about classification. In addition to learning about classification, coaches valued understanding the athlete and their impairment to effectively fulfill their coaching roles.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".