Trajectories of psychosocial symptoms and wellbeing in asylum seekers and refugees exposed to traumatic events and resettled in Western Europe, Turkey, and Uganda
Bibliographic record
Abstract
Background: Longitudinal studies examining mental health trajectories in refugees and asylum seekers are scarce.Objectives: To investigate trajectories of psychological symptoms and wellbeing in refugees and asylum seekers, and identify factors associated with these trajectories.Method: 912 asylum seekers and refugees from the control arm of three trials in Europe (n = 229), Turkey (n = 320), and Uganda (n = 363) were included. We described trajectories of psychological symptoms and wellbeing, and used trauma exposure, age, marital status, education, and individual trial as predictors. Then, we assessed the bidirectional interactions between wellbeing and psychological symptoms, and the effect of each predictor on each outcome controlling for baseline values.Results: Symptom improvement was identified in all trials, and for wellbeing in 64.7% of participants in Europe and Turkey, versus 31.5% in Uganda. In Europe and Turkey domestic violence predicted increased symptoms at post-intervention (ß = 1.36, 95% CI 0.17–2.56), whilst murder of family members at 6-month follow-up (ß = 1.23, 95% CI 0.27–2.19). Lower wellbeing was predicted by murder of family member (ß = −1.69, 95% CI −3.06 to −0.32), having been kidnapped (ß = −1.67, 95% CI −3.19 to −0.15), close to death (ß = −1.38, 95% CI −2.70 to −0.06), and being in the host country ≥2 years (ß = −1.60, 95% CI −3.05 to −0.14). In Uganda at post-intervention, having been kidnapped predicted increased symptoms (ß = 2.11, 95% CI 0.58–3.65), and lack of shelter (ß = −2.51, 95% CI −4.44 to −0.58) and domestic violence predicted lower wellbeing (ß = −1.36, 95% CI −2.67 to −0.05).Conclusion: Many participants adapt to adversity, but contextual factors play a critical role in determining mental health trajectories.
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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".