Athletes from Great Britain report greater doping likelihood than Greek and Italian athletes: A cross-sectional survey of over 4,000 athletes
Bibliographic record
Abstract
• Over 4000 athletes from three countries completed a measure of doping likelihood. • Men reported greater doping likelihood than women. • Non-Olympic Sport athletes report greater doping likelihood than Olympic Sport athletes. • Athletes from Great Britain reported greater doping likelihood than Greek and Italian athletes. In the past twenty years, a large body of research has examined who is more likely to dope as a function of participant variables, such as gender, sport type, and competition level. However, this research is limited as studies are often conducted on modest sample sizes from one country. To overcome this issue, we recruited a large sample of athletes across three countries to examine differences in doping likelihood as a function of participant variables. Athletes ( N = 4,644) were recruited from Great Britain ( n = 2,505), Greece ( n = 1,044), and Italy ( n = 1,095) and asked to complete an anonymous measure of doping likelihood. Results indicated that doping likelihood scores were greater in men than women, for athletes competing in non-Olympic sports (e.g., American football, kickboxing, netball) than Olympic sports (e.g., Athletics, basketball, football) and in British athletes than both Greek and Italian athletes. We found an interaction between country and competitive level. Specifically, in Great Britain, higher competitive level athletes reported greater doping likelihood than lower competitive level athletes, which was not found for Greek and Italian athletes. Our results highlight that athletes report greater doping likelihood for those that are 1) from Great Britain, 2) men, and 3) participating in non-Olympic sports. We also show that differences in doping likelihood between competition levels may differ depending on country of residence .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".