Social network exposure and sociodemographic factors associated with intentions to use anabolic-androgenic steroids
Bibliographic record
Abstract
Objectives This study aimed to examine 1) perceived anabolic-androgenic steroid (AAS) use within the social networks of non-consumers, 2) the intentions to use AAS among non-consumers, including the sociodemographic predictors of intentions to use, and 3) whether perceived AAS use within one’s social network is associated with intentions to use. Methods Data from 1515 boys and men from Canada and the United States were analyzed. Descriptive statistics were used to describe perceived AAS use within the social networks of participants and their intentions to use AAS. Linear regression analyses were used to determine the sociodemographic associations with intentions to use AAS and whether perceived AAS use within one’s social network is associated with AAS use intentions. Results Over one third (34.9 %) of participants reported perceiving any individual in their social network who uses AAS, while intentions to use AAS were relatively weak. Participants who identified as multi-racial were associated with stronger intentions to use AAS, while those who identified as gay or had a master’s degree or higher were associated with weaker intentions to use. Participants who perceived anyone in their social network who used AAS were associated with stronger intentions to use themselves. Conclusion These findings provide novel insights into AAS use within the social networks of a large, diverse, and international sample of boys and men, and their intentions to use. Prevention efforts should focus on boys and men who identify as multi-racial and who have individuals in their social network who use AAS.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".