“We’re all in this together. We’re a real team.” Perceptions of powerchair sport in Canada
Bibliographic record
Abstract
Parasport offers athletes with disabilities the opportunity to develop their physical fitness, connect socially, and improve their overall well-being. Yet, athletes with high support needs (AHSN) who use motorized wheelchairs as their primary mode of mobility have limited opportunities to engage in parasport. Barriers to participation are varied and include a lack of powerchair sport (PCS) programming and lack of consideration for the unique disability-related requirements of AHSN. Therefore, investigation of current PCS programming is required to better understand how quality experiences are facilitated and may be replicated to promote participation in PCS. To achieve this aim, we conducted semi-structured interviews with 17 PCS participants. An interpretive approach to reflexive thematic analysis was taken to analyze interview transcripts. Two overarching themes were identified. First, the unique needs of AHSN that must be recognized to facilitate quality PCS experiences related to the extended team; the disability community; and the progression of the athlete’s condition as well as their sport classification. Second, quality experiences are facilitated and constrained by the structure and function of PCS programming. Quality PCS programs enable participants to perceive themselves as athletes engaged in a “real team.” In other cases, ineffective leadership hinders athletes’ participation and negatively impacts their experience quality. Together, these findings highlight ways in which PCS programming can be leveraged to promote sport participation by persons with high support needs. These findings also add to a growing body of literature exploring the experiences of AHSN.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".