Enumeration of Sex Workers in the Central Business District of Nairobi, Kenya
Bibliographic record
Abstract
Accurate program planning for populations most at risk for HIV/STI acquisition requires knowledge of the size and location where these populations can best be reached. To obtain this information for sex workers operating at 137 hotspots in the central business district (CBD) in Nairobi, Kenya, we utilized a combined mapping and capture-recapture enumeration exercise. The majority of identified hotspots in this study were bars. Based on this exercise, we estimate that 6,904 male and female sex workers (95% confidence intervals, 6690 and 7118) were working nightly in the Nairobi CBD in April 2009. Wide ranges of captures per spot were obtained, suggesting that relatively few hot spots (18%) contain a relatively high proportion of the area's sex workers (65%). We provide geographic data including relatively short distances from hotspots to our dedicated sex worker outreach program in the CBD (mean<1 km), and clustering of hotspots within a relatively small area. Given the size covered and areas where sex work is likely taking place in Nairobi, the estimate is several times lower than what would be obtained if the entire metropolitan area was enumerated. These results have important practical and policy implications for enhancing HIV/STI prevention efforts.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 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".