Systematic review of mental health problems and migration stressors among Kurdish migrants in western host countries
Bibliographic record
Abstract
OBJECTIVES: This systematic review aimed to evaluate the mental health outcomes of Kurdish migrants, with particular attention to the prevalence of psychological disorders and the impact of pre- and post-migration stressors. METHODS: A systematic search was conducted in PubMed, Scopus, and Google Scholar according to the PRISMA guidelines. The strategy combined medical subject headings (MeSH) and relevant keywords on Kurdish migrants, refugees, asylum seekers, and mental health. The search yielded 132 records, of which 15 studies met the eligibility criteria, representing a total of 5,319 participants. The methodological quality and risk of bias of the included studies were assessed using the Newcastle-Ottawa Scale. RESULTS: Following migration and resettlement in host countries, Kurdish migrants were found to experience high rates of PTSD (36.9%), depression (36.3%), and anxiety (27.7%), together with additional difficulties such as insomnia, fatigue, and suicidal ideation. Pre-migration was most often driven by war and political oppression (81.1%), violence and persecution (60.7%), and economic hardship (59.1%). Post-migration stressors included family separation (47%), discrimination and violence (51.4%), isolation and loneliness (51.7%), economic difficulties (40%), fear of deportation (21%), and other problems (30%). CONCLUSION: As one of the largest stateless and historically persecuted populations, Kurds experience distinctive challenges in their migration journeys. Their significant burden of mental health problems underscores the need for culturally tailored and trauma-informed interventions that address both displacement experiences and barriers to integration in host societies, as well as during deportation and reintegration into their home countries.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".