Barriers and facilitators to voluntary HIV testing uptake among communities at high risk of HIV exposure in Chennai, India
Bibliographic record
Abstract
In India, increasing uptake of voluntary HIV testing among 'core risk groups' is a national public health priority. While HIV testing uptake has been studied among key populations in India, limited information is available on multi-level barriers and facilitators to HIV testing, and experiences with free, publicly available testing services, among key populations. We conducted 12 focus groups (n = 84) and 12 key informant interviews to explore these topics among men who have sex with men, transgender women, cisgender female sex workers, and injecting drug users in the city of Chennai. We identified inter-related barriers at social-structural, health-care system, interpersonal, and individual levels. Barriers included HIV stigma, marginalised-group stigma, discrimination in health-care settings, including government testing centres, and fears of adverse social consequences of testing HIV positive. Facilitators included outreach programmes operated by community-based/non-governmental organisations, accurate HIV knowledge and risk perception for HIV, and access to drug dependence treatment for injecting drug users. Promoting HIV testing among these key populations requires interventions at several levels: reducing HIV-related and marginalised-group stigma, addressing the fears of consequences of testing, promoting pro-testing peer and social norms, providing options for rapid and non-blood-based HIV tests, and ensuring non-judgmental and culturally competent HIV counselling and testing 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".