An experimental test of the efficacy of gain- and loss-framed messages for doping prevention in adolescent athletes
Bibliographic record
Abstract
Doping is a prevalent issue, not only among Olympians and professional athletes; young athletes and those at the sub-elite level have reported doping as well. Doping programs have been developed to target adolescent athletes and prevent doping initiation. The efficacy of primary doping prevention initiatives may be enhanced with health communication strategies, such as message framing. To date, there have been very few studies examining message framing among adolescents and none in the context of doping prevention. The purpose of this study was to compare the efficacy of gain-framed and loss-framed messages on key psychological antecedents of doping among adolescent athletes. In a randomized controlled trial, 133 athletes aged 12 to 16 years old (Mage = 13.73; 53% boys) from a variety of sports viewed either a gain- or loss-framed video. Intentions, attitudes, self-efficacy, and perceived norms were all assessed immediately before and after the videos. Mixed between-within subjects ANOVAs revealed no differential influence for either message frame on changes in any of the outcomes. Attitudes, self-efficacy, and perceived norms all increased significantly over time for participants in both conditions. Overall, no strong evidence is provided to support definitive recommendations regarding optimal message framing for doping prevention; however, the findings suggest that regardless of message frame, a brief messaging intervention may still have a beneficial influence on psychological constructs related to doping.Acknowledgments: This project was carried out with the support of the World Anti-Doping Agency
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".