Engaging adolescent athletes with effective messaging to prevent doping in sport
Bibliographic record
Abstract
Adolescent athletes' use of nutritional supplements and banned performance-enhancing substances is a concerning issue in sport. Comprehensive anti-doping programs use doping prevention messages to educate and influence adolescents' doping attitudes and behaviour. The purpose of this descriptive study was to explore factors that could facilitate adolescent athletes' engagement with doping prevention messages through a brief educational video. One hundred and thirty-three adolescent athletes, aged 12 to 16 years (Mage = 13.73; 53% boys) were exposed to an anti-doping video message, and completed pre- and post-video questionnaires assessing demographics, sport participation, and psychosocial predictors of doping. A series of exploratory statistical tests were conducted to draw three recommendations for message development in future anti-doping programs. First, messages need to be relatable to the athletes. Participants were significantly more likely to learn new information or be interested in the video if they rated it as more relatable. Second, messages need to be targeted by gender. Boys reported significantly higher positive outcome expectations than girls, which may increase the risk of doping given boys view the benefits of doping more favorably. Third, messages should not only address banned substances, but educate athletes about the risks associated with nutritional supplements as they can provide a gateway to doping for adolescents. Nutritional supplement use was significantly more prevalent among males, older athletes, and more competitive athletes, suggesting the need to address this issue particularly among these groups. Anti-doping programs could be enhanced by including more engaging and effective doping prevention messages for adolescent 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".