Trauma exposure and PTSD prevalence among Yazidi, Christian and Muslim asylum seekers and refugees displaced to Iraqi Kurdistan
Bibliographic record
Abstract
BACKGROUND: There is unreliable, and negligible information on the mental health and trauma-exposure of asylum-seekers and displaced refugees in the Iraqi Kurdistan region. OBJECTIVES: To evaluate how responsible the ethno-religious origins are, for the prevalence of trauma exposure and post-traumatic stress disorder (PTSD) in displaced Iraqi asylum-seekers and refugees residing in the Iraqi Kurdistan region. METHODS: Structured interviews with a cross-sectional sample of 150 individuals, comprised of three self-identified ethno-religious groups (50 participants in each): Christians, Muslims, and Yazidis. RESULTS: 100% prevalence of trauma exposure and 48.7% of current PTSD among refugees, 70% PTSD rate of Yazidi participants, which is significantly higher (p < 0.01) compared to 44% of Muslim participants and 32% of Christian participants. These findings were corroborated using the self-rated PTSD, DSM-5 Checklist, with more severe PTSD symptom scores (p < 0.001) obtained among Yazidis (43.1; 19.7), compared to Muslims (31.3; 20.1) and Christians (29.3; 17.8). Self-rated depressive symptoms (Patient Health Questionnaire-9) were also higher (p < 0.007) among Yazidis (12.3; 8.2) and Muslims (11.7; 5.9), compared to Christians (8.1; 7).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".