Scale-up and coverage of Avahan: a large-scale HIV-prevention programme among female sex workers and men who have sex with men in four Indian states
Bibliographic record
Abstract
OBJECTIVE: Documenting the implementation of a public health programme as per its design is critical to interpretation of results from survey-led outcome and impact evaluation activities, the authors describe the scale-up and coverage of large-scale HIV-prevention services provided to female sex workers (FSWs) and high-risk men who have sex with men (HR-MSM) during the first 5 years of the Avahan programme in India. METHODS: Implementing NGO partner-generated denominator estimates from 70 districts were used to estimate the programme's intended coverage. Routine programme-monitoring data until December 2008 were used to describe the service and commodity availability, service utilisation to generate internal estimates of coverage. Coverage was validated in few districts using data from a cross-sectional survey. RESULTS: In December 2008, the estimated denominators for intended services were about 217,000 FSWs and 80,000 HR-MSM. By January 2007, 79% of eventual total clinics and 75% drop-in centres were established, and 83% of eventual peer educators were active. By month 48, sufficient condoms to cover all estimated FSW commercial sex acts were distributed free. By month 60, 75% of the estimated denominator intended to be covered was met monthly. 86% of FSWs and 67% of HR-MSM ever contacted had used sexually transmitted infections services at least once. Cross-sectional survey generated coverage results suggest that programme-monitoring data provide a proxy to coverage of services. CONCLUSION: Avahan's monitoring data show that Avahan achieved infrastructure scale by year 3 and high contact coverage through peers and with commodities by year 5 of implementation as per the design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".