Generational differences in sexual behaviour and partnering among gay, bisexual, and other men who have sex with men
Bibliographic record
Abstract
INTRODUCTION: Given that different generations of gay, bisexual, and other men who have sex with men (gbMSM) have been influenced by substantially different life course events and cultural contexts, we explored differences in sexual behaviour between millennials, Gen-Xers, and baby boomers. METHODS: Sexually active gbMSM from Metro Vancouver, ≥16 years, were recruited using respondent-driven sampling between 2012-2015 and completed computer-assisted self-interviews every 6 months, up to 2017. To explore differences between generations (millennials born ≥1987, Gen-Xers born 1962-1986, baby boomers born <1962) we used multivariable logistic regression models using baseline, RDS-weighted data. We also examined 6-month trends, stratified by generation, in partner number, prevalence of high-risk sex, and relationship status using hierarchical mixed-effects models. RESULTS: =0.002). After controlling for relevant demographics, differences were observed for some sexual behaviours (i.e., anal sex positioning, giving oral sex, sex toys, masturbation, sexual app/website use, transactional sex) but not others (i.e., receiving oral sex, rimming, fisting, watersports, group sex). At baseline, millennials reported less high-risk sex than other generations but all trended toward less high-risk sex, fewer partners, and regular partnering over the course of the study. CONCLUSIONS: While there was notable similarity across generations, millennial gbMSM reported earlier age at first anal intercourse and less high-risk sex. However, all generations trended towards less high-risk sex, fewer partners, and regular partnering over time.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".