Water and food insecurity and linkages with physical and sexual intimate partner violence among urban refugee youth in Kampala, Uganda: cross-sectional survey findings
Bibliographic record
Abstract
Abstract Water insecurity (WI) and food insecurity (FI), each associated with violence exposure, are understudied in urban humanitarian settings. We conducted a cross-sectional survey with urban refugee youth in Kampala, Uganda to examine: (a) social-ecological correlates of WI, FI, and concurrent FI and WI; (b) associations between WI and FI with recent sexual and physical intimate partner violence (IPV); and (c) associations between an Index of Vulnerability (IoV) comprised of social-ecological stressors (e.g., FI, WI) and recent physical/sexual IPV. Among participants (n = 340; mean age: 21.1 years, standard deviation: 2.6) almost half (47.8%) reported WI and two-thirds (65.0%) FI. In adjusted analyses, time in Uganda, age, and insecure housing were associated with increased odds of WI and concurrent FI and WI; household toilet sharing and insecure housing were associated with increased odds of FI. In adjusted analyses, WI, concurrent FI and WI, housing insecurity, and parenthood were associated with higher sexual IPV odds. FI and parenthood were associated with increased odds of physical IPV. IoV scores were associated with physical/sexual IPV, and IoV scores accounted for more variance in physical/sexual IPV than any individual indicator. Future research can address WI and co-occurring resource insecurities to reduce gender-based water violence risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".