Scenes as Micro-Cultures: Examining Heterogeneity of HIV Risk Behavior Among Gay, Bisexual, and Other Men Who Have Sex with Men in Toronto, Canada
Bibliographic record
Abstract
Using latent class analysis (LCA), we examined patterns of participation in multiple scenes, how sexual risk practices vary by scene, and psychosocial factors associated with these patterns among 470 gay, bisexual, and other men who have sex with men (GBM) recruited from Toronto. We calculated posterior probability of being in a class from participation in nine separate scenes. We used Entropy, the Bayesian information criterion and the Lo–Mendel–Rubin likelihood ratio test to identify the best fit model. Fit indices suggested a four-class solution. Half (50%) of the GBM reported no or minimal participation in any scene, 28% reported participating in the dance club scene, 16% reported participating in the BDSM, bear, and leather scenes, and 6% reported participating in circuit, party and play, and sex party scenes. Compared to GBM who did not participate in scenes, GBM participating in the BDSM-Bear-Leather scene were more likely to be older, white, to report higher sexual self-esteem, and to engage in condomless anal sex; Party and Play scene members were more likely to be of Asian origin, and to use drugs before and during sex, whereas Dance Club scene members were more likely to be younger and to report lower self-esteem but higher hope. LCA allowed us to identify distinct social niches or micro-cultures and factors characterizing these micro-cultures. GBM differ in their risk for HIV and STIs according to characteristics associated with participation in distinct micro-cultures associated with scenes. Tailored interventions may be needed that focus on reducing HIV risk and promoting sexual health in specific contexts such as the BDSM-Bear-Leather and Party and Play.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".