Mental Health Disparities Among Homosexual Men and Minorities: A Systematic Review
Bibliographic record
Abstract
Mental health disparities in sexual minorities, particularly homosexual and bisexual men, are a significant public health concern. This study examines six key themes: general psychiatric issues, health services, minority stress, trauma and PTSD, substance and drug misuse, and suicidal ideation. The aim is to provide a comprehensive synthesis of the evidence, identify potential intervention and prevention strategies, and address knowledge gaps in understanding the unique experiences of homosexual and bisexual men. Reported as per the PRISMA Statement 2020 guidelines, PubMed, PsycINFO, Web of Science, and Scopus were searched until February 15, 2023, with no language restrictions. A combination of the following keywords and MeSH terms was used: homosexual, bisexual, gay, men who have sex with men, mental health, psychiatric disorders, health disparities, sexual minorities, anxiety, depression, minority, stress, trauma, substance, drug misuse, and/or suicidality. Out of 1,971 studies located through database searching, 28 were included in this study pooling a total of 199,082 participants from the United States, the United Kingdom, Australia, China, Canada, Germany, the Netherlands, Israel, Switzerland, and Russia. Thematic findings of all the studies were tabulated and thereby synthesized. Addressing mental health disparities in gay, bisexual men, and sexual minorities requires evidence-based, comprehensive approaches, culturally competent care, accessible services, targeted prevention strategies, community-based support, public awareness, routine screenings, and research collaboration. This inclusive, research-informed approach can effectively reduce mental health issues and enable optimal well-being in these 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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".