Assessing reported condom use among female sex workers in southern India through examination of condom availability
Bibliographic record
Abstract
OBJECTIVES: A key indicator of success of HIV prevention programmes is the number of female sex worker (FSW) sex acts protected by condoms. This measure usually relies on FSW reports, which may be biased. We examined condom availability data in five Karnataka districts to estimate the proportion of FSW sex acts potentially protected by condoms. METHODS: Financial, programme, population, condom and contraceptive data were obtained from governmental and non-governmental sources, surveys and HIV prevention programmes. Sexual behaviour data were obtained from general population and FSW surveys. We examined four scenarios in a sensitivity analysis, each one assuming different proportions of available condoms that might have been used by sex workers. RESULTS: Possible condom use rates for all FSW sex acts ranged from 3%-36% in 2004 to 56%-96% in 2008. The two most realistic scenarios that discounted the number of private sector condoms that might have been bought for sex acts other than with FSWs showed that 16-24% of FSW sex acts could have been protected by condoms in 2004 rising to 77-85% in 2008. CONCLUSIONS: These data suggest that condom availability in these Karnataka districts in 2004 was low in relation to the number of FSW sex acts, but rose substantially over the ensuing 4 years. Condom availability data can be useful for triangulation with other available data, such as self-reported condom use, to provide a range of possibilities regarding the number of FSW sex acts protected by condoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".