Application of the “syndemics” theory to explain unprotected sex and transactional sex: A crosssectional study in men who have sex with men (MSM), transgender women, and non-MSM in Colombia
Bibliographic record
Abstract
INTRODUCTION: Men who have sex with men (MSM) and transgender women (TW) in Colombia are highly affected by HIV. To improve understanding of the role of HIV risk behaviors in HIV acquisition, we used the syndemic framework, a useful concept to inform prevention efforts. OBJECTIVE: To examine the effect of four psychosocial conditions, namely, forced sex, history of childhood sexual abuse, frequent alcohol use, and illicit drug use on unprotected sex and the synergistic effects ("syndemic" effects) of these conditions on HIV risk behavior. MATERIALS AND METHODS: We enrolled a total of 812 males (54.7% men who have sex with men, MSM; 7.3% transgender women, and 38% non-MSM). The participants were recruited from neighborhoods of low socioeconomic status through free HIV-counseling and -testing campaigns. We performed Poisson regression analysis to test the associations and interactions between the four psychosocial conditions and unprotected sex with regular, occasional, and transactional partners. To test the "syndemic" model, we assessed additive and multiplicative interactions. RESULTS: The prevalence of any psychosocial condition was 94.9% in transgender women, 60.1% in MSM, and 72.2% in non-MSM. A higher likelihood of transactional sex was associated in MSM (prevalence ratio (PR)=7.41, p<0.001) and non-MSM (PR=2.18, p< 0.001) with three or all four conditions compared to those with one condition. Additive interactions were present for all combinations of psychosocial problems on transactional sex n MSM. No cumulative effect or additive interaction was observed in transgender women. CONCLUSIONS: Our study highlights the need for bundled mental health programs addressing childhood sexual abuse, illicit drug use, and frequent alcohol use with other HIV prevention programs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".