Practitioner perspectives of athlete recovery in paralympic sport
Bibliographic record
Abstract
Athlete health and sport performance research for athletes with disabilities has increased substantially over the years as the level of competition and intensity in Paralympic sport has grown. However, relative to able-bodied sport, there remains some key areas of parasport research which are distinctly lacking. Athlete recovery, as a counterbalance to training stress and an important factor in preventing adverse health consequences such as illness and injury, is one of these understudied areas for elite para-athletes. Thus, the purpose of this descriptive qualitative study was to understand factors impacting recovery among Paralympic athletes, based on practitioner perspectives, with the aim of providing insightful guidance for applied practice. Semi-structured interviews were conducted with 15 North American sport practitioners who worked with elite para-athletes. Through thematic analysis, five main themes about optimizing athlete recovery in various populations of para-athletes were developed: a) prioritize the simple concepts, b) get to know the whole athlete, c) experience matters, d) musculoskeletal factors, and e) non-training load. Collectively, these results highlight how humanistic approaches to care, augmented by individual athlete expertise, extensive education, and a consideration of fundamental lifestyle factors is exceedingly important for para-athlete recovery. This study further describes that the approach to recovery among para-athletes, a diverse population, is uniquely complex from that of able-bodied sport and warrants scholarly attention.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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".