An Explanatory Model of Doping Susceptibility Examining Morality in Elite Track and Field Athletes: A Logistic Regression Analysis
Bibliographic record
Abstract
The aim of the present study was to develop an explanatory model of doping susceptibility among competitive track and field athletes using a logistic regression analysis accounting for some morality-related variables which were not explored in previous studies. A total of 281 Spanish elite track and field athletes (49.5% women, 48.4% have competed with the national team) completed an online survey measuring different constructs in relation to doping susceptibility. The final model demonstrated that nutritional supplements (OR: 2.39; CI: 1.16–4.90; p < 0.05), moral disengagement (OR: 2.17; CI: 1.48–3.19; p < 0.001), acceptance of gamesmanship (OR: 1.29; CI: 1.12–1.49; p < 0.001), and descriptive norms (OR: 1.21; CI: 1.04–1.41; p < 0.05) are the factors better explaining doping susceptibility. The profile of the athlete at risk of being more susceptible to doping is represented by someone who is aged under 20 years, believes that doping is present in his/her sport, has positive attitudes of acceptance of gamesmanship, is morally disconnected from doping, and frequently consumes nutritional supplements. It is recommended to deliver education related to the use of sports supplements and potential ill-effects of performance-enhancing substances or methods, and to engage athletes in doping prevention programs at an early age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".