Performance-Enhancing Substance Use and Sexual Risk Behaviors among U.S. Men: Results from a Prospective Cohort Study
Bibliographic record
Abstract
Performance-enhancing substance (PES) use is common among young men and prior research has documented cross-sectional associations between anabolic-androgenic steroid (AAS) use and sexual risk behaviors. However, this relationship remains understudied among a longitudinal cohort of young adult men, and research on prospective associations between legal PES (e.g., creatine) use and sexual risk behaviors is lacking. The current study addressed these oversights using data from the National Longitudinal Study of Adolescent to Adult Health (N = 5,451). AAS use and legal PES use were assessed at Wave III (ages 18-26) and 10 indicators of sexual risk behavior were assessed at seven-year follow up (Wave IV; ages 24-32). Linear and logistic regression analyses were conducted adjusting for demographic and behavioral confounders. Participants who reported AAS use and legal PES use had significantly higher number of one-time sexual partners and higher odds of multiple sex partners around the same time in the past 12 months. Participants who reported AAS use had higher odds of any STI in the past 12 months. These results extend prior research on the risk behaviors and adverse effects of PES use. Health care professionals should assess for PES use among young adult men and provide guidance on healthful sexual behaviors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".