Water insecurity and sexual and gender-based violence among refugee youth: qualitative insights from a humanitarian setting in Uganda
Bibliographic record
Abstract
Abstract Refugee youth disproportionately experience sexual and gender-based violence (SGBV) and water insecurity, yet their SGBV experiences in the context of water insecurity are understudied. In this qualitative study, we conducted six focus groups (n = 48) and in-depth individual interviews (IDI) (n = 12) with refugee youth aged 16–24, and IDI with refugee elders (n = 8) in Bidi Bidi Refugee Settlement, Uganda. We applied thematic analysis informed by a social contextual framework and found that (1) SGBV is gendered, whereby adolescent girls and young women (AGYW) were targets for violence (symbolic context), and is intertwined with gender norms linked to AGYW's water collection roles (relational context); (2) water scarcity and off-site access to water infrastructure, combined with limited lighting, provide insecure environments that exacerbate AGYW's SGBV risks (material context); (3) participant generated solutions to water insecurity-related SGBV included engaging men and communities in dialogue and water collection (relational context), technology (e.g., solar lighting), improved security, and additional water points (material context). Findings signal the need to integrate water and sanitation hygiene development with SGBV prevention and sexual health (e.g., post-rape care) interventions. Refugee youth and communities should be meaningfully engaged in developing contextually relevant, gender transformative services to mitigate SGBV risks and advance health and rights.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".