Athletes with intellectual impairments and their support personnel’s experience of anti-doping
Bibliographic record
Abstract
No research has examined the anti-doping experiences of athletes with intellectual impairments. We responded to this gap by interviewing athletes and their support personnel (e.g. parents and coaches) about their experience of anti-doping policy and practice. International athletes (N = 10; 20% female) with intellectual impairments and their support personnel (N = 16; 50% female) volunteered to participate in the study. Using an interpretive paradigm, participants attended an online, semi-structured interview. Data were analysed using reflexive thematic analysis and three higher-order themes were created. First, while athletes had an awareness of anti-doping and believed it was important to stop cheating, they found it difficult to understand more complex anti-doping policies and practices and questioned why they themselves needed to be drug tested. Second, anti-doping education was inaccessible and as a result, many reported negative emotional distress during drug testing and difficulty engaging with educational resources. Finally, a number of best practices were outlined for those with intellectual impairments, including ensuring anti-doping control staff are empathetic and considerate of the athletes’ needs, shortening, simplifying and repeating educational sessions over the year, and educating support personnel alongside their athlete to offer help throughout the season. In conclusion, anti-doping policy and practice needs to be adapted and tailored for athletes with intellectual impairments to help increase their understanding of their anti-doping rights and responsibilities.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".