Study of an Anti-Doping Education Program in Spanish Sports Sciences Students
Bibliographic record
Abstract
Doping continues to be one of the biggest risks to the credibility of elite sports, and its practice remains widespread among athletes despite improved controls. Athletes' support personnel could be key to preventing doping behavior. In this sense, anti-doping education for this population appears as a possible strategy to reduce doping behaviors in elite sport, but these programs must be evaluated and designed based on scientific evidence. The aim of this research is to explore the impact of an anti-doping education program about substances perceived efficacy, ill-health short- and long-term effects, and the morality of doping substance use in Spanish sports sciences students. METHOD: A total of 145 students of Physical Activity and Sport Sciences (PASS) from different Spanish universities who took an online anti-doping educational course of the Spanish Anti-Doping Commission (CELAD) answered a questionnaire on their perceptions about doping before, after, and four months later. RESULTS: The results show that the course reduced students' ignorance about the effects of substances on performance and health and increased their moral judgment and feelings against doping. DISCUSSION: The results are in line with previous research that showed that the moral stance against doping can be improved through educational programs. CONCLUSION: Online educational interventions can be effective in reducing doping behavior, so their future implementation among ASP can be an effective strategy to reduce doping behavior.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".