Mental Health Challenges and Barriers to Care Among Arab Refugees: A Scoping Review of US and Canadian Studies
Bibliographic record
Abstract
Introduction: Displacement due to conflict is a hallmark of humanitarian crises and traumatized survivors face unique health challenges. The purpose of this study is to investigate the health factors impacting Arab refugees in the United States and Canada, with a focus on the mental health challenges that further complicate the health care experiences of this vulnerable population. Methods: A scoping review was conducted using a protocol based on the Preferred Reporting items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. We established eligibility criteria for selecting original peer-reviewed articles published in English between January 1, 1990 and December 31, 2024. The search utilized five databases (PubMed, Embase, Scopus, CINAHL Complete, and ProQuest). We selected 31 articles based on study criteria. Results: The literature consistently highlights a high burden of mental health disorders among Arab refugees-particularly posttraumatic stress disorder, depression, and anxiety. Additionally, despite insurance coverage, psychological service utilization remains low due to systemic health barriers. Conclusion: Arab refugees in the United States and Canada face significant mental health challenges that are compounded by barriers such as stigma, language obstacles, and inadequate access to culturally sensitive care. Addressing these specific needs can improve health outcomes at both individual and community levels.
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".