Moral Disengagement, Social Norms, and Motivational Profiles Influence Attitudes Toward Doping Among Spanish Athletics Coaches
Why this work is in the frame
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Bibliographic record
Abstract
Coaches strongly influence athletes' attitudes toward doping and can shape athlete's beliefs, behaviors, and decisions to be for or against doping. Coached-centered studies examining multiple factors affecting coaches' doping attitudes and behavior are scarce. The aim of this study was to analyze for the first-time attitudes toward doping in athletics coaches using the Sport Drug Control Model (SDCM) as a theoretical framework. A secondary aim was to determine the factors in the model predicting attitude and susceptibility toward doping. A cross-sectional study was carried out using a sample consisting of 201 Spanish athletics competitive level coaches from whom 11.4% were female. Participants completed a cross-sectional online survey. Structural equation modeling showed a good fitness of the SDCM. Positive attitudes toward doping predicted high susceptibility to doping (β = 0.39, p < 0.001). Moral disengagement (β = 0.58, p < 0.001), descriptive norms (β = 0.42, p = 0.001), ego-oriented goals (β = 0.34, p < 0.05), and self-efficacy to refrain from doping (β = 0.26, p < 0.05) displayed a significant influence on attitudes toward doping. Self-reported doping prevalence in coaches was 4.5%. These variables should be considered when designing anti-doping research projects and educational programs aiming at modifying coaches' attitudes toward doping. It is recommended to focus more efforts on coaches, without putting aside the athletes, and therefore turn coaches into reliable doping prevention factors. To this end, it is necessary to enhance scientific research and then develop, implement, and promote more educational programs targeting coaches, on a mandatory basis while covering the specific needs of coaches so that they can perform their role as anti-doping educators in an effective, committed, and proactive manner.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it