A QUESTION MODULE FOR ASSESSING COMMUNITY STIGMA TOWARDS HIV IN RURAL INDIA
Bibliographic record
Abstract
This paper describes a simple question module to assess community stigma in rural India. Fear of stigma is known to prevent people from seeking HIV testing and to contribute to further disease transmission, yet relatively little attention has been paid to community stigma and ways of measuring it. The module, based on a vignette of a fictional HIV-positive woman, was administered to 494 married women and 186 unmarried male and female adolescents in a village in rural Maharashtra, India. To consider the usefulness of the question module, a series of hypotheses were developed based on the correlations found in other studies between HIV-related stigma and socio-demographic characteristics (age, education, discussion of HIV with others, knowing someone living with HIV, knowledge about its transmission and whether respondents acknowledged stigmatizing attitudes as their own or attributed them to others). Many of the study's hypotheses were confirmed. Among married women, correlates of stigma included older age, lack of discussion of HIV and lack of knowledge about transmission; among adolescents, lower education and lack of discussion of HIV were the most significant correlates. The paper concludes that the question module is a useful tool for investigating the impact of interventions to reduce stigma and augment social support for people living with HIV in rural India.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".