Sociodemographic factors associated with trajectories of depression among urban refugee youth in Kampala, Uganda: A longitudinal cohort study
Bibliographic record
Abstract
Background: There is a high prevalence of depression among refugee youth in low- and middle-income countries, yet depression trajectories are understudied. This study examined depression trajectories, and factors associated with trajectories, among urban refugee youth in Kampala, Uganda. Methods: We conducted a longitudinal cohort study with refugee youth aged 16-24 in Kampala, Uganda. We assessed depression using the Patient Health Questionnaire-9 and conducted latent class growth analysis (LCGA) to identify depression trajectories. Sociodemographic and socioecological factors were examined as predictors of trajectory clusters using multivariable logistic regression. Results: Data were collected from n = 164 participants (n = 89 cisgender women, n = 73 cisgender men, n = 2 transgender persons; mean age: 19.9, standard deviation: 2.5 at seven timepoints; n = 1,116 observations). Two distinct trajectory clusters were identified: "sustained low depression level" (n = 803, 71.9%) and "sustained high depression level" (n = 313, 28.1%). Sociodemographic (older age, gender [cisgender women vs. cisgender men], longer time in Uganda), and socioecological (structural: unemployment, food insecurity; interpersonal: parenthood, recent intimate partner violence) factors were significantly associated with the sustained high trajectory of depression. Conclusions: The chronicity of depression highlights the critical need for early depression screening with urban refugee youth in Kampala. Addressing multilevel depression drivers prompts age and gender-tailored strategies and considering social determinants of health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".