Examining the Influence of Impairment Type on the Development of Paralympic Sport Athletes
Bibliographic record
Abstract
Research has recently examined the role of impairment onset on athlete development in Paralympic sport; however, less is known on how impairment type can impact athlete sporting pathways. In this study, 187 Australian and Canadian Paralympic sport athletes completed a survey. Participants were divided into the following four groups: impaired muscle power (n = 79); ataxia, athetosis, and hypertonia (n = 44); limb deficiencies (n = 42); and other physical impairments (n = 22). Mechanisms of initiation into Paralympic sport varied between groups with some drawn to sport through friends and/or family (i.e., limb deficiencies and other physical impairments groups) while others through talent search programs (i.e., ataxia, athetosis, and hypertonia group) or health care professionals/rehabilitation centers (i.e., impaired muscle power group). Results revealed no significant differences between groups in the chronological age or absolute years for achieving milestones. However, considering the high variability within the sample, more research is necessary to better understand how athletes with different physical impairments navigate through their sporting careers.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".