Chemsex Session Typologies and Associated Sociodemographic Factors in Sexual Minority Men: Latent Class Analysis From a Cultural Perspective Using a Cross-Sectional Survey
Bibliographic record
Abstract
Background: Chemsex prevalence is still not well known, and both the lack of homogeneity and cultural component of chemsex practices are usually overlooked. Objective: This study aims to estimate the proportion of sexual minority men (SMM) engaging in chemsex sessions, while understanding the cultural dimension of chemsex, and to analyze distinct session typologies with potential risk differences and the sociodemographic factors associated with engaging in them. Methods: A total of 5711 SMM residing throughout Spain participated in an anonymous web-based survey that assessed chemsex session engagement and characteristics, drug use, and sociodemographic variables. We measured the association of sociodemographic factors with engaging in chemsex sessions by calculating adjusted prevalence ratios, using multivariate Poisson regression analysis. Chemsex typologies were analyzed using latent class analysis, and sociodemographic factors were associated with the different risk classes. Results: Our results determined that 21.1% (1205/5711; 95% CI 20.0%-22.1%) of SMM engaged in chemsex sessions during their lifetime. Participating in sessions was significantly associated with being a migrant, not having a comfortable financial situation, openly living their sexuality, residing in bigger municipalities, older age, using steroids, and living with HIV (adjusted prevalence ratio: range 1.17-2.01; all P values <.05). Three typologies of sessions with different risks were identified with latent class analysis, with 23.2% of SMM engaging in sessions taking part in higher-risk ones, which was associated with younger age, using steroids, living in bigger municipalities, openly living their sexuality, and living with HIV, compared to SMM engaging in lower-risk sessions (odds ratio: range 2.75-4.99). Conclusions: Chemsex is relatively common among SMM in Spain, but it is important to differentiate typologies of sessions with varying risks, and the proportion of SMM engaging in high-risk sessions is low. Chemsex is highly associated with sociodemographic factors. Chemsex should be prioritized in public health programs, which should consider the different forms of sessions with their varying risks and prevalence, while also considering the cultural dimension inherent to chemsex.
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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 | 0.003 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".