Implicit doping attitude and athletes' accuracy in avoiding unintentional doping when being offered beverages with banned performance-enhancing substances
Bibliographic record
Abstract
OBJECTIVES: Implicit doping attitude reflects the strength of one's automatic or unconscious evaluation of doping. This cross-societal study examined how implicit doping attitude predicted athletes' accuracy in avoiding unintentional doping. DESIGN: A real-time experimental design with cross-sectional data from three geographical regions. METHODS: = 28.21, SD = 8.43; female = 47.1 %) from Hong Kong (N = 177), Australia (N = 164) and the United Kingdom (N = 340) completed two real-time experimental tasks for measuring their implicit doping attitude (a brief single-category implicit association test) and the accuracy of avoiding unintentional doping (a novel canned beverage sorting task). RESULTS: = 0.03, 95 % CI of β = -1.64 to -0.15) associated with athletes' accuracy in avoiding unintentional doping by screening out beverages with banned performance-enhancing substances. This association was maintained when we statistically controlled for the effects of society. CONCLUSIONS: Athletes with a positive implicit doping attitude were less accurate in determining whether unknown beverages with the possible presence of banned performance-enhancing substances should be consumed. The negative association between implicit doping and athletes' accuracy in avoiding unintentional doping appeared to be consistent across societies.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".