Association Between System Reach and Exposure to Interventions and Characteristics of Mobile Female Sex Workers in Four High HIV Prevalence States in India
Bibliographic record
Abstract
Mobility among Female Sex Workers (FSWs) interrupts their demand for, and utilization of, health services under any intervention. Various strategic interventions are meant to provide access to care and reduce the incidence of HIV and other STIs among FSWs. This paper applies a bivariate probit regression analysis to explain the probability of mobile FSWs being reached by the system and being exposed to interventions jointly with a wide variety of characteristics of mobile FSWs in India. The data used are based on a cross-section survey among 5,498 mobile FSWs in 22 districts of four high HIV prevalence states in southern India. A majority of mobile FSWs (59%) were street-based and about 70 percent of them were members of SW organization and nearly half (46%) were highly mobile. The majority of them (90%) had been contacted by outreach workers from any system in the last two years in their current location and 94 percent were exposed to interventions in terms of getting free or subsidized condoms. Bivariate probit analysis revealed that comprehensive interventions are able to reach more vulnerable mobile FSWs effectively, e.g. new entrants, highly mobile, reported STIs, tested for HIV ever and serving a high volume of clients. The results complement the efforts of government and other agencies in response to HIV. However, the results highlight that specific issues related to various subgroups of this highly vulnerable population remain unaddressed calling for tailoring the response to the specific needs of the sub-groups.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".