Violence against Women Living with HIV: A Cross Sectional Study in Nepal
Bibliographic record
Abstract
BACKGROUND: Violence against Women (VAW) and Human Immunodeficiency Virus (HIV) both constitute major public health issues and there is an increasing evidence of their intersection. Data are sparse on the intersection of VAW and HIV in South Asia region. We aimed to identify different forms and magnitude of violence incurred by women living with HIV, and analyse causes and consequences. METHODS: A cross-sectional study was conducted among 43 HIV positive women in three districts of Nepal, in the period of March-May 2008. Data was collected through semi-structured interview questionnaire. RESULTS: The vast majority of the participants (93.02%) had suffered from at least one form of the violence. The prevalence of violence rose up sharply after being diagnosed with HIV positive than before (93.02% vs.53.5%). Forty-five percent of the participants reported their husbands being main perpetrator of violence. Self-humiliation and health and treatment problem were the major consequences of violence as reported by 90% and 77.5% of the participants respectively. CONCLUSION: Violence was observed to be highly prevalent among women living with HIV in Nepal. Further larger and nationally representative researches are imperative to better understand the cross-section between VAW and HIV. Our finding recommends to prioritizing programs on social aspects of HIV such as violence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".