Longitudinal Event-Level Analysis of Gay and Bisexual Men’s Anal Sex Versatility: Behavior, Roles, and Substance Use
Bibliographic record
Abstract
Gay and bisexual Men Who Have Sex with Men (GBM) are sexually unique in that they can practice penile-anal sex versatility, i.e. engage in insertive and receptive anal sex. Individual-level versatility is extensively researched both as a sexual behavior linked to HIV/STI transmission, and as a GBM identity that can change over time. However, there is a dearth of research on event-level versatility (ELV), defined as taking the receptive and insertive role in the same sexual encounter. We analyzed event-level data from 644 GBM in the Momentum Health Study from February 2012-February 2017 to identify factors associated with ELV prevalence, the relationship between ELV and anal sex role preference, and sero-adaptive and sexualized drug use strategies. Univariate analysis revealed ELV prevalence rates between 15% and 20%. A multivariate generalized linear mixed model indicated ELV significantly (p < .05) associated with versatile role preference and condomless sex. However, the majority of ELV came from GBM reporting insertive or receptive role preferences, and there was significantly higher condom use among sero-discordant partners, indicating sero-adaptation. Multivariate log-linear modeling identified multiple polysubstance combinations significantly associated with ELV. Results provide insights into GBM sexual behavior and constitute empirical data useful for future HIV/STI transmission pattern modeling.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".