Associations Between Extreme Weather Events and Resource Insecurities With HIV Vulnerabilities and Biomedical HIV Prevention Outcomes Among Adolescent Girls and Young Women in Kenya: A Cross-Sectional Analysis
Bibliographic record
Abstract
ObjectivesWe examined associations between extreme weather events (EWE), resource insecurities, and HIV vulnerabilities among a purposive sample of adolescent girls and young women (AGYW) aged 16 to 24 in Nairobi and Kisumu, Kenya.MethodsWe conducted multivariable logistic/linear regression on cross-sectional survey data to assess associations between EWE exposure, food insecurity (FI), water insecurity (WI), and sanitation insecurity (SI) with HIV vulnerabilities (transactional sex [TS], intimate partner violence [IPV], sexual relationship power [SRP], and preexposure prophylaxis [PrEP] awareness and acceptability).ResultsAmong participants (n = 597; mean age: 20.13 years; standard deviation = 2.5), in adjusted analyses, SI and WI were associated with increased TS. Increased cumulative EWEs and eco-anxiety were associated with increased IPV. EWE frequency, FI, and SI were associated with reduced SRP. EWE frequency and SI were associated with reduced, and WI with increased, PrEP awareness. EWE frequency and SI were associated with PrEP acceptability.ConclusionResource scarcities and EWEs were associated with HIV vulnerabilities and PrEP acceptability among AGYW.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".