Partners and clients of female sex workers in an informal urban settlement in Nairobi, Kenya
Bibliographic record
Abstract
This paper compares and contrasts number of partners and condom use behaviour for female sex workers and a sample of women working in other economic activities, with both samples drawn from the large informal settlement of Kibera, Nairobi. As expected, univariate analysis revealed much higher numbers of overall sexual partners and higher levels of condom use among female sex workers compared to Kibera women in other occupations. An unexpected finding, however, was that female sex workers with a romantic partner had significantly fewer sexual partners per unit time than female sex workers without such a partner. This finding held for multivariate analysis, with negative binomial regression analyses showing that having a romantic partner was significantly associated with reductions in total number of both sexual partners overall and with sexual partners who did not use condoms. In contrast, HIV status, education, number of immediate family members and levels of alcohol consumption were non-significant factors for both regression analyses. Results suggest that female sex workers' romantic partners act as more than sources of possible HIV infection; rather, romantic partners appear to have an important positive impact on health. We discuss this finding in light of possible harm-reduction programmes focusing on female sex workers and their romantic partners.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".