P708 Women’s encounters with venue-based HIV risk contexts in abuja, nigeria
Bibliographic record
Abstract
<h3>Background</h3> Venues where people meet sexual partners are understood to be important locations where HIV transmission risk plays out and represent potential intervention points. Women involved in sex work and those seeking casual partners spend time in the same venues, forming sexual partnerships with some of the same people and experiencing the same risks. This study provides a characterization of key venues where women meet new sexual partners in Abuja, Nigeria, and describes the sexual behaviours, sexual networking patterns, and challenges experienced by women in these venues. <h3>Methods</h3> Key informant interviews were used to characterize 836 venues where people congregate for social activities in Abuja, Nigeria, in terms of number of patrons, busy times, and availability of harm reduction supplies. A questionnaire capturing demographics, behaviours, health, and experiences of violence was administered to 892 women who participate in sex work or casual sex at a random sample of 105 of the profiled venues. Descriptive analysis was conducted with stratification by type of venue. <h3>Results</h3> A diverse set of venues were identified, with bars/nightclubs identified as having the highest volume of patrons. Most of the women indicated meeting partners at bars/nightclubs as well as hotels/lodges. Half of the women had experienced a miscarriage or abortion and perceived themselves to be at great risk of HIV infection. Eighteen percent had experienced condom breakage in the previous week, 15% had ever been arrested, and 8% had been beaten in the past year. <h3>Conclusion</h3> A diverse set of women intermingle at different venues and have a diverse set of needs, including reproductive health, violence reduction, and infectious disease prevention. By re-orienting HIV programs towards venues where sexual partnerships form instead of towards specific key populations who are often blamed for transmission, the broader needs of a larger group of individuals who attend these venues may be addressed. <h3>Disclosure</h3> No significant relationships.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".