Cango Lyec (Healing the Elephant): HIV Prevalence and Vulnerabilities Among Adolescent Girls and Young Women in Postconflict Northern Uganda
Bibliographic record
Abstract
OBJECTIVES: Adolescent girls and young women younger than 25 years (AGYW) account for disproportionate HIV infections in sub-Saharan Africa. Impacts of war in Northern Uganda continue to affect HIV-related health and wellbeing of young people postconflict. Prevalence and incidence of HIV infection were estimated, and factors associated with HIV prevalence among sexually active AGYW in Northern Uganda were investigated. METHODS: Cango Lyec is a cohort involving conflict-affected populations in Northern Uganda. Nine randomly selected communities in Gulu, Nwoya, and Amuru districts were mapped. House-to-house census was conducted. Consenting participants aged 13-49 years were enrolled over 3 study rounds (2011-2015), of whom 533 were AGYW and had ever had sex. Data were collected on trauma, depression, and sociodemographic-behavioral characteristics. Venous blood was taken for HIV and syphilis serology. Multivariable logistic regression determined baseline factors associated with HIV prevalence. RESULTS: HIV prevalence among AGYW was 9.7% (95% CI: 7.3 to 12.6). AGYW living in Gulu (adjusted risk ratio, aRR: 2.48; 95% CI: 1.12 to 5.51) or Nwoya (aRR: 2.65; 95% CI: 1.03 to 6.83) were more likely than in Amuru to be living with HIV. Having self-reported genital ulcers (aRR: 1.93; 95% CI: 0.97 to 3.85) or active syphilis (aRR: 3.79; 95% CI: 2.35 to 6.12) was associated with increased risk of HIV infection. The likelihood of HIV was higher for those who experienced sexual violence in the context of war (aRR: 2.37; 95% CI: 1.21 to 4.62) and/or probable depression (aRR: 1.95; 95% CI: 1.08 to 3.54). HIV incidence was 8.9 per 1000 person-years. CONCLUSION: Ongoing legacies of war, especially gender violence and trauma, contribute to HIV vulnerability among sexually active AGYW. Wholistic approaches integrating HIV prevention with culturally safe initiatives promoting sexual and mental health in Northern Uganda are essential.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".