Effective Intervention Features of a Doping Prevention Program for Athletes: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
This study systematically reviewed the effectiveness of cognitive, affective, and combined approaches in doping prevention, considering the impact of athletes’ active versus passive participation. Following the PRISMA 2020 guidelines and the PICOS framework, a literature search identified ten studies involving 3581 athletes (1094 women, 2487 men). Ten studies were included as a sample in the meta-analysis and meta-regression, which were used in the effect size calculation. This meta-analysis shows that anti-doping education programs effectively improve short-term doping intentions (ES = 0.29, p < 0.001) and anti-doping behaviors (ES = −0.27, p < 0.001), although there is a decline in the long-term effects (ES = −0.13, p = 0.009). Moral behaviors were unaffected (ES = 0.01, p < 0.001), suggesting that changing deeper values requires alternative approaches like mentorship. Passive participation negatively impacted doping intentions (ES = −0.40, p = 0.004) and behaviors (ES = −0.40, p = 0.022), highlighting the need for active engagement. Pre-experimental designs showed a negative effect on behaviors (ES = −0.74, p = 0.023), emphasizing the importance of rigorous methodologies. While anti-doping education programs effectively influence short-term attitudes and intentions, sustaining behavioral change requires continuous reinforcement and active engagement. The decline in the long-term effects suggests that standalone interventions are insufficient to instill lasting anti-doping behaviors in athletes.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".