Population Size Estimation of Venue-Based Female Sex Workers in Ho Chi Minh City, Vietnam: Capture-Recapture Exercise
Bibliographic record
Abstract
BACKGROUND: There is limited population size estimation of female sex workers (FSWs) in Ho Chi Minh City (HCMC)-the largest city in Vietnam. Only 1 population size estimation among venue-based female sex workers (VFSWs) was conducted in 2012 in HCMC. Appropriate estimates of the sizes of key populations are critical for resource allocation to prevent HIV infection. OBJECTIVE: The aim of this study was to estimate the population size of the VFSWs from December 2016 to January 2017 in HCMC, Vietnam. METHODS: A multistage capture-recapture study was conducted in HCMC. The capture procedures included selection of districts using stratified probability proportion to size, mapping to identify venues, approaching all VFSWs to screen their eligibility, and then distribution of a unique object (a small pink makeup bag) to all eligible VFSWs in all identified venues. The recapture exercise included equal probability random selection of a sample of venues from the initial mapping and then approaching FSWs in those venues to determine the number and proportion of women who received the unique object. The proportion and associated confidence bounds, calculated using sampling weights and accounting for study design, were then divided by the number of objects distributed to calculate the number of VFSWs in the selected districts. This was then multiplied by the inverse of the proportion of districts selected to calculate the number of VFSWs in HCMC as a whole. RESULTS: Out of 24 districts, 6 were selected for the study. Mapping identified 573 venues across which 2317 unique objects were distributed in the first capture. During the recapture round, 103 venues were selected and 645 VFSWs were approached and interviewed. Of those, 570 VFSWs reported receiving the unique object during the capture round. Total estimated VFSWs in the 6 selected districts were 2616 (95% CI 2445-3014), accounting for the fact that only 25% (6/24) of total districts were selected gives an overall estimate of 10,465 (95% CI 9782-12,055) VFSWs in HCMC. CONCLUSIONS: The capture-recapture exercise provided an estimated number of VFSWs in HCMC. However, for planning HIV prevention and care service needs among all FSWs, studies are needed to assess the number of sex workers who are not venue-based, including those who use social media platforms to sell services.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".