Associations between climate change-related factors and sexual health: A scoping review
Bibliographic record
Abstract
There is growing attention to the ways in which climate change may affect sexual health, yet key knowledge gaps remain across global contexts and climate issues. In response, we conducted a scoping review to examine the literature on associations between climate change and sexual health. We searched five databases (May 2021, September 2022). We reviewed 3,183 non-duplicate records for inclusion; n = 83 articles met inclusion criteria. Of these articles, n = 30 focused on HIV and other STIs, n = 52 focused on sexual and gender-based violence (GBV), and n = 1 focused on comprehensive sexuality education. Thematic analysis revealed that hurricanes, drought, temperature variation, flooding, and storms may influence HIV outcomes among people with HIV by constraining access to antiretroviral treatment and worsening mental health. Climate change was associated with HIV/STI testing barriers and worsened economic conditions that elevated HIV exposure (e.g. transactional sex). Findings varied regarding associations between GBV with storms and drought, yet most studies examining flooding, extreme temperatures, and bushfires reported positive associations with GBV. Future climate change research can examine understudied sexual health domains and a range of climate-related issues (e.g. heat waves, deforestation) for their relevance to sexual health. Climate-resilient sexual health approaches can integrate extreme weather events into programming.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".