Long-term participation in parasport: Current issues, challenges and future directions
Bibliographic record
Abstract
In recent decades, the Paralympics movement has seen tremendous growth (Radtke & Doll-Tepper, 2014). Parallel to this, research in Parasport has been on the rise. Given the importance of long-term sport participation to physical and mental health, it is vital to develop a deep understanding of nuances associated with factors that influence attraction, engagement, initiation and maintenance of sport involvement (see Penedo & Dahn, 2005 for a review). While these factors have been extensively explored and reported in able-bodied sports, the same cannot be said for Parasport (Dehghansai et al., 2017). To a degree, some Paralympic sport organizations have a tendency to adapt and adopt developmental models from their able-bodied sport counterparts (Hutzler, Higgs, & Legg, 2016). However, considering the nuances associated with athletes' impairments, the introduction of this element into an already dynamic and complex model can increase variability and arguably constrain how athletes negotiate through their experiences in sport. The purpose of this symposium is to highlight key issues pertaining to the development of athletes with impairments, highlight current gaps in our understanding and identify directions for future research. More specifically, through the lens of the athletes, coaches and administrators, we will explore factors that impact individuals' transition, initial engagement, and sustained participation in Parasport with the aim to contextualize key elements vital to athletes' experiences in sport across their career.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".