Associations between water insecurity and depression among refugee adolescents and youth in a humanitarian context in Uganda: cross-sectional survey findings
Bibliographic record
Abstract
BACKGROUND: Water insecurity is linked to poor mental health through intrapersonal, relational and community-based stressors. We examined water insecurity and depression among refugee youth in Bidi Bidi, Uganda. METHODS: We conducted a cross-sectional survey and multivariable ordinal logistic regression to examine associations between water insecurity and depression severity, adjusting for gender, resilience, social support and food insecurity. RESULTS: Among participants (n=115; mean age: 19.7 y, SD 2.3), 80.0% reported water insecurity and 18.3% had moderate/severe depression symptoms. Water insecurity was independently associated with higher levels of depression severity (adjusted OR: 5.61; 95% CI 1.20 to 26.30; p=0.03). CONCLUSIONS: Findings suggest water insecurity was commonplace and associated with depression. Water insecurity could be integrated in refugee mental health promotion by policymakers and community-based programmers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".