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
<p>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 <em>Entropy</em>, 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 <em>BDSM</em>-<em>Bear</em>-<em>Leather</em> scene were more likely to be older, white, to report higher sexual self-esteem, and to engage in condomless anal sex; <em>Party and Play</em> scene members were more likely to be of Asian origin, and to use drugs before and during sex, whereas <em>Dance Club</em> 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 <em>BDSM</em>-<em>Bear</em>-<em>Leather</em> and <em>Party and Play</em>.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".