Climate change and extreme weather events and linkages with HIV outcomes: recent advances and ways forward
Bibliographic record
Abstract
PURPOSE OF REVIEW: Discuss the recent evidence on climate change and related extreme weather events (EWE) and linkages with HIV prevention and care outcomes. RECENT FINDINGS: We identified 22 studies exploring HIV prevention and care in the context of EWE. HIV prevention studies examined sexual practices that increase HIV exposure (e.g., condomless sex, transactional sex), HIV testing, and HIV recent infections and prevalence. HIV care-related outcomes among people with HIV included clinical outcomes (e.g., viral load), antiretroviral therapy adherence and access, HIV care engagement and retention, and mental and physical wellbeing. Pathways from EWE to HIV prevention and care included: structural impacts (e.g., health infrastructure damage); resource insecurities (e.g., food insecurity-related ART adherence barriers); migration and displacement (e.g., reduced access to HIV services); and intrapersonal and interpersonal impacts (e.g., mental health challenges, reduced social support). SUMMARY: Studies recommended multilevel strategies for HIV care in the context of EWE, including at the structural-level (e.g. food security programs), health institution-level (e.g., long-lasting ART), community-level (e.g. collective water management), and individual-level (e.g., coping skills). Climate-informed HIV prevention research is needed. Integration of EWE emergency and disaster preparedness and HIV services offers new opportunities for optimizing HIV prevention and care.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".