The prevalence and correlates of depression before and after the COVID-19 pandemic declaration among urban refugee adolescents and youth in informal settlements in Kampala, Uganda: A longitudinal cohort study
Bibliographic record
Abstract
PURPOSE: There is scant research examining urban refugee youth mental health outcomes, including potential impacts of the COVID-19 pandemic. We examine prevalence and ecosocial risk factors of depression in the periods before and after the COVID-19 pandemic declaration among urban refugee youth in Kampala, Uganda. METHODS: Data from a cohort of refugee youth (n = 367) aged 16-24 years were collected in periods before (February 2020) and after (December 2020) the WHO COVID-19 pandemic declaration. We developed crude and adjusted generalized estimating equation logistic regression models to examine demographic and ecosocial factors (food insecurity, social support, intimate partner violence) associated with depression, and include time-ecosocial interactions to examine if associations differed before and after the pandemic declaration. RESULTS: The prevalence of depression was high, but there was no significant difference before (27.5%), and after (28.9%) the pandemic declaration (P = .583). In adjusted models, food insecurity (aOR: 2.54; 95% CI: 1.21-5.33) and experiencing violence (aOR: 2.53; 95% CI: 1.07-5.96) were associated with increased depression, and social support was associated with decreased depression (aOR: 0.85; 95% CI: 0.81-0.89). CONCLUSIONS: These findings highlight the urgent need for interventions to address chronic depression, food insecurity, and ongoing effects of violence exposure among urban refugee youth in Kampala.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".