Minoritized and Poorly Understood: A Scoping Review of Mental and Physical Health Among Arab Adolescents in Canada and the United States
Bibliographic record
Abstract
Arab adolescents are both racialized and invisible minorities in Canada and the United States (US), following the war on terror, incomplete ethnic categorization, Islamophobia, and anti-Arab racism. We conducted a scoping review of physical and psychological health in Arab adolescent populations living in the US and Canada. Inclusion criteria encompassed adolescents and emerging adults aged 10-24 who identified as Arab or having Arab identity and Southwest Asia and North Africa (SWANA) origins. Included scholarly literature reported at least one physical or psychological health outcome and was published in English or Arabic up until 2025. We identified over 200 relevant studies using PubMed, Web of Science, ResearchRabbit.ai, Google Scholar, and Undermind AI. We reviewed 50 total studies that met our inclusion criteria, highlighting the paucity of research on health and biopsychosocial variation among Arab adolescents in North America over a 30-year period. Despite heterogeneity in the health outcomes reported across studies, many focused on acculturative stress, ethnic identity formation, mental health, and discrimination. Few studies examined physical health and sexual and reproductive health; none examined pubertal, immunological, or linear growth outcomes. We discuss how biocultural and human biological research approaches can contribute to advancing a needed and more holistic understanding of health variation among Arab adolescent populations.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".