Intimate partner violence is associated with incident HIV infection in women in Uganda
Bibliographic record
Abstract
OBJECTIVES: To quantify the association between intimate partner violence (IPV) and incident HIV infection in women in the Rakai Community Cohort Study between 2000 and 2009. DESIGN AND METHODS: Data were from the Rakai Community Cohort Study annual surveys between 2000 and 2009. Longitudinal data analysis was used to estimate the adjusted incidence rate ratio (IRR) of incident HIV associated with IPV in sexually active women aged 15-49 years, using a multivariable Poisson regression model with random effects. The population attributable fraction was calculated. Putative mediators were assessed using Baron and Kenny's criteria and the Sobel-Goodman test. RESULTS: Women who had ever experienced IPV had an adjusted IRR of incident HIV infection of 1.55 (95% CI 1.25-1.94, P = 0.000), compared with women who had never experienced IPV. Risk of HIV infection tended to be greater for longer duration of IPV exposure and for women exposed to more severe and more frequent IPV. The adjusted population attributable fraction of incident HIV attributable to IPV was 22.2% (95% CI 12.5-30.4). There was no evidence that either condom use or number of sex partners in the past year mediated the relationship between IPV and HIV. CONCLUSION: IPV is associated with incident HIV infection in a population-based cohort in Uganda, although the adjusted population attributable fraction is modest. The prevention of IPV should be a public health priority, and could contribute to HIV prevention.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".