Pre-Settlement and Post-Settlement Stressors and Mental Illness Among Migrants from War-Torn Countries in the Middle East: A Scoping Review
Bibliographic record
Abstract
The rising influx of Middle Eastern immigrants from war-torn countries into Canada, the United States, Europe, and Australia underscores the urgent need to understand the stressors they face during pre-settlement and post-settlement phases and their consequent impact on mental health. This scoping review addresses this gap by exploring 16 existing studies on Middle Eastern immigrants' experiences in these regions published since 1995. The studies were identified through database searches and selected based on specific inclusion and exclusion criteria focused on Middle Eastern immigrants from war-torn countries resettling in North America, Australia, and Europe. The review reveals that immigrants and refugees encounter significant challenges during resettlement, including acculturative stress, loss of status, and existential struggles. These stressors contribute to higher prevalence rates of psychological disorders compared to the general population. Examining the 16 studies predominantly from North America, Australia, and Europe, our review underscores the complex interplay between pre-settlement and post-settlement stressors and mental health outcomes among Middle Eastern immigrants. However, the limited scope of current research highlights the pressing need for further investigation across different continents and regions.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".