Understanding the Influence of Proximal Networks on High School Athletes’ Intentions to Use Androgenic Anabolic Steroids
Bibliographic record
Abstract
Understanding what influences adolescent athletes is important for managers designing anti-doping initiatives. It is commonly assumed that elite athletes who dope influence adolescent athletes to similarly dope. Using the theory of normative social behavior, the effect of norms on adolescent athletes’ intentions to use steroids was examined. The social distance between respondents and the source of normative information was systematically varied to include four separate levels (friends, teammates, college athletes, professional athletes). Data were collected from 404 male adolescent athletes. Participants indicated their intentions to use steroids and their perceptions of descriptive and injunctive norms of referent others. Descriptive and injunctive norms were predictive of intentions to use steroids with the magnitude of explained variance greater with more proximal referents. Adolescent athletes’ intentions to use steroids are influenced by social norms. Moreover, the social distance of referents is consequential. Interventions strategies should incorporate teammates and friends, rather than professional 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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".