Sexual and gender minority health in the Middle East and North Africa Region: A scoping review
Bibliographic record
Abstract
Background: Researchers in studies from multiple countries suggest that sexual and gender minority people experience high rates of violence, stigma, and discrimination, as well as mistrust of health care providers and systems. Despite growing evidence related to sexual and gender minority health in North America and Europe, we know little about the health of this population in the Middle East and North Africa. Objectives: We aimed to comprehensively examine the literature related to the health of sexual and gender minority people in the Middle East and North Africa and to identify research gaps and priorities. Design: We conducted a scoping review informed by the framework recommended by Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) tool. Data sources: We searched the following databases: PubMed (using Medline All on the Ovid platform), PsycINFO (Ovid), CINAHL (Ebsco), and Embase (Ovid). The search strategy combined terms for the geographic region of interest (Middle East and North Africa) and the population of interest (sexual and gender minority). Each was operationalized using multiple search terms and, where available, controlled vocabulary terms. Review Methods: Research articles were identified and assessed for inclusion using an explicit strategy. Relevant information was extracted and synthesized to present a descriptive summary of existing evidence. Results: = 32). Five themes emerged from the review: sexual health (52; 53%); mental health (20; 20%); gender identity (17; 17%); violence and discrimination (7; 7%); and experiences with the healthcare system (2; 2%). Although researchers focused on multiple health outcomes in some studies, we included them under the theme most closely aligned with the main objective of the study. Conclusion: Although our study is limited to few countries in the Middle East and North Africa region, we found that sexual and gender minority individuals face multiple adverse sexual and mental health outcomes and experience high rates of stigma, discrimination, and violence. More research is needed from countries outside of Lebanon, Pakistan, and Iran, including community-based participatory approaches and multi-level intervention development. Nurses and other healthcare providers in the region need training in providing inclusive care for this population.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".