Cultivating clean sport environment with athlete support personnel (ASP): A study on anti-doping knowledge, attitudes, and practices of ASP
Bibliographic record
Abstract
Athlete support personnel (ASP) work closely with, treat, or assist an athlete participating in or preparing for sports competition. Their involvement in preventing and eliminating doping is crucial. This study investigated the knowledge, attitudes, and practices related to doping in sports among ASP from Southeast Asian countries. An anonymized self-administered questionnaire assessing knowledge, attitudes, and practices related to doping in sports issues was administered to ASP from Southeast Asian countries. Overall, 596 respondents from eleven countries participated in the study. The majority were male (67.1%), non-healthcare professionals (89.4%), and retired elite athletes (57.7%). Their knowledge was found to be poor, reflected in a mean score of 16.1±5.4 out of 30. Attitudes towards doping, as measured by the Performance Enhancement Attitude Scale (PEAS), scored 18.1±9.4, indicating a negative attitude. While some respondents provided information on medication and supplements use in sports to athletes, only 11.8% reported regular updates on doping in sports topics. Meanwhile, the knowledge and PEAS scores were significantly different between the genders (p = 0.04; p = 0.02). The knowledge score was also negatively correlated with the PEAS (p<0.01). This study highlights significant knowledge gaps among ASP in Southeast Asia regarding anti-doping practices. Enhancing their knowledge and fostering positive attitudes toward anti-doping efforts can promote a culture of doping-free sports, particularly among the emerging generation of young athletes they support.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".