Associations Between Anabolic-Androgenic Steroid Use and Sexual Health Behaviors Among Adolescent Boys: Results From the 2019 Youth Risk Behavior Survey
Bibliographic record
Abstract
The aim of this study was to determine the association between lifetime anabolic-androgenic steroid (AAS) use and seven indicators of sexual health behaviors among a nationally representative sample of adolescent boys in the United States. Multiple modified Poisson regression analyses were conducted to determine the associations between any lifetime AAS use and seven indicators of sexual health behaviors among 2,095 sexually active adolescent boys from the 2019 National Youth Risk Behavior Survey. Sexually active boys who reported lifetime AAS use were at greater risk of having sexual intercourse before the age of 13 years (adjusted risk ratio [aRR] = 2.73, 95% confidence interval [CI] = [1.44, 5.17]), reporting ≥4 sexual partners in their lifetime (aRR = 1.96, 95% CI = [1.34, 2.89]) and in the past 3 months (aRR = 6.77, 95% CI = [3.19, 14.37]), having been tested for HIV in their lifetime (aRR = 2.49, 95% CI = [1.13, 4.73]), and having been tested for any sexually transmitted infection in the past 12 months (aRR = 3.14, 95% CI = [1.63, 6.03]). These findings align with prior research among adult men and have implications for public health and health care prevention efforts to reduce the use of AAS, as well as support the engagement in safe sexual health behaviors among adolescent boys.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".