Associations between extreme weather events and HIV vulnerabilities among refugee youth in a Ugandan refugee settlement: cross-sectional survey findings
Bibliographic record
Abstract
Background There is growing evidence of associations between extreme weather events (EWE) and HIV vulnerabilities, yet this is understudied in humanitarian settings. We examined associations between EWE and HIV vulnerabilities among refugee youth in Bidi Bidi Refugee Settlement, Uganda. Methods We collected baseline (February to March 2024) cohort data with refugee youth aged 16-24years in Bidi Bidi. We conducted linear and logistic regression to assess associations between (a) number of past-year EWE types (e.g. extreme rain/flooding, changes in expected rain patterns, drought, extreme heat, fire, changes in expected temperature), and (b) frequency of past-year EWE, with HIV vulnerabilities (sexual relationship power, reproductive autonomy, condom use self-efficacy, transactional sex, intimate partner violence, multiple sex partners), adjusted for age, gender, education and employment. Results Among 400 participants (50% women; mean age: 19 years, standard deviation: 2.3), a higher number of past-year EWE types (vs 1) was significantly associated with reduced sexual relationship power (2-4 EWE: adjusted beta [aβ] = -2.96, P =0.009; ≥5 EWE: aβ = -4.92, P P =0.006; ≥5 EWE: aβ = -0.42, P =0.001) and condom use self-efficacy (2-4 EWE: aβ = -3.02, P P P =0.040), intimate partner violence (≥5 EWE: aOR 3.13, P =0.007) and multiple sex partners (≥5 EWE: aOR 4.70, P =0.002). Increased EWE frequency was significantly associated with lower sexual relationship power, reproductive autonomy and condom use self-efficacy. Conclusions EWE experiences were associated with multiple HIV vulnerabilities among refugee youth. Climate-informed, youth-tailored HIV prevention strategies are urgently needed.
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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 | 0.003 |
| 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) | 0.002 | 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".