Physiological Considerations to Support Podium Performance in Para-Athletes
Bibliographic record
Abstract
The twenty-first century has seen an increase in para-sport participation and the number of research publications on para-sport and the para-athlete. Unfortunately, the majority of publications are case reports/case series or study single impairment types in isolation. Indeed, an overview of how each International Paralympic Committee classifiable impairment type impact athlete physiology, health, and performance has not been forthcoming in the literature. This can make it challenging for practitioners to appropriately support para-athletes and implement evidence-based research in their daily practice. Moreover, the lack of a cohesive publication that reviews all classifiable impairment types through a physiological lens can make it challenging for researchers new to the field to gain an understanding of unique physiological challenges facing para-athletes and to appreciate the nuances of how various impairment types differentially impact para-athlete physiology. As such, the purpose of this review is to (1) summarize how International Paralympic Committee classifiable impairments alter the normal physiological responses to exercise; (2) provide an overview of "quick win" physiological interventions targeted toward specific para-athlete populations; (3) discuss unique practical considerations for the para-sport practitioner; (4) discuss research gaps and highlight areas for future research and innovation, and (5) provide suggestions for knowledge translation and knowledge sharing strategies to advance the field of para-sport research and its application by para-sport practitioners.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".