Experiences of drought, heavy rains, and flooding and linkages with refugee youth sexual and reproductive health in a humanitarian setting in Uganda: qualitative insights
Bibliographic record
Abstract
Climate-related extreme weather events (EWE) exacerbate resource insecurities that, in turn, shape sexual and reproductive health (SRH). Refugee settlements face increased EWE exposure yet are understudied in EWE research. We explored experiences of climate change and SRH among refugee youth aged 16–24 in Bidi Bidi Refugee Settlement, Uganda. This qualitative study involved walk-along individual youth interviews and key informant (KI) service provider interviews. We conducted thematic analysis informed by the resource scarcity framework, which explores socioeconomic and ecological risks for resource insecurity. Participants (N = 44) included youth (n = 32; mean age: 20.0, standard deviation [SD]: 2.4; 50% men, 50% women) and KI (n = 12; mean age: 37.0, SD: 5.8; 75% men, 25% women). Findings illustrate how EWE shape SRH outcomes for refugee young women: (1) climate change contributes to water scarcity, extreme heat, and changing rain patterns; (2) drought contributes to resource scarcities (e.g. food, water) that increase sexual and gender-based violence (SGBV) risks, transactional sex, and menstruation insecurity and (3) heavy rains/flooding contribute to resource scarcities that increase SGBV risks, and sanitation insecurity exacerbates menstruation insecurity. Findings highlight how EWE-related resource insecurities are associated with poor SRH (STI/HIV acquisition risks, unplanned pregnancy, SGBV) and should be addressed in multi-level climate-informed humanitarian programmes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".