War and HIV: Sex and gender differences in risk behaviour among young men and women in post-conflict Gulu District, Northern Uganda
Bibliographic record
Abstract
Despite growing knowledge of the dynamics of HIV infection during conflict, far less is known about the period that follows cessation of hostilities and its implications for population health. This study sought to fill a lacuna in epidemiological evidence by examining HIV infection and related vulnerabilities of young people living in resource-scarce, post-emergency transit camps that are now home to thousands of displaced people following two decades of war in northern Uganda. In 2010, a cross-sectional demographic and behavioural survey was conducted with 384 transit camp residents aged 15-29 years old in Gulu District. Biological specimens were collected for rapid and confirmatory HIV testing. Separate multivariable logistic regression models by sex identified risk factors for HIV infection. HIV prevalence was 15.6% (95% confidence interval [CI]: 10.8%, 21.6%) among females and 9.9% (95% CI: 6.1%, 15.0%) among males. The strongest correlate of HIV infection among men was a non-consensual sexual debut (adjusted odds ratio [AOR] 3.24; 95% CI: 1.37-7.67), and having practiced dry sex (AOR 7.62; 95% CI: 1.56-16.95) was the strongest correlate among women. Conflict-affected men and women experience vulnerability to HIV infection in different ways than may have originally been understood. Post-conflict programme planners must therefore design and implement contextualised, evidence-based responses to HIV that are sensitive to gender and cultural issues.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".