Prevalence and associated factors of selling sex among men who have sex with men (MSM) in Latin America: results from the Latin American MSM Internet Survey in 18 countries (LAMIS-2018)
Bibliographic record
Abstract
INTRODUCTION: Selling sex has been associated with negative social and health outcomes, but most studies have been limited geographically and have not distinguished between selling and buying sex. This study assesses prevalence and factors associated with selling sex in the last 12 months among men who have sex with men (MSM) in 18 Latin American countries. METHODS: Data were collected in 2018 through the Latin American MSM Internet Survey, a cross-sectional online survey. Of 64 655 participants, 9585 were excluded due to data inconsistencies on age and partner status, and 1728 due to missing outcome data, yielding an analytic sample of 53 342. Multivariable logistic regression was used for analysis. RESULTS: Overall, 6.9% (10.3% among MSM aged 18-24) reported selling sex in the previous year. Higher odds of selling sex were associated with younger age, low education, being born abroad, low financial coping, substance use, potential alcohol dependency, early sexual debut with a male partner, low sexual agency and sex with women. High educational level and having a steady male partner were associated with lower odds. CONCLUSIONS: Key factors associated with selling sex among MSM in Latin America include socioeconomic, behavioural and relational variables. Harm reduction and preventive interventions may be particularly needed among younger MSM. Codeveloping these interventions with the MSM community can ensure sustainability, relevance and strengthen providers' ability to offer individualised, respectful care. Longitudinal and qualitative studies are needed to monitor long-term health and tailor interventions to individual needs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".