Severe Mental Illness in LGBT Populations: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: There is increasing attention to diversity in psychiatric services and widespread recognition of the mental health implications of stigma for individuals from sexual or gender minority groups. However, these areas remain markedly underdeveloped in the area of severe mental illness. The aim of this review was to map out the existing base of knowledge in these areas to help inform future research, practice, and policy directions. METHODS: A review of the literature was conducted to answer the following question: What factors and strategies need to be considered when developing services for individuals from sexual or gender minority groups who are experiencing severe mental illness? A comprehensive search of MEDLINE, PsycINFO, and Google Scholar was completed by using Arksey and O'Malley's methodological framework for scoping reviews. RESULTS: A total of 27 publications were identified for review. Mental health services research indicated generally lower levels of service satisfaction among lesbian, gay, bisexual, transgender, and transsexual (LGBT) individuals and minimal evidence regarding specific interventions. Descriptive research suggested an increased risk of severe mental illness in LGBT populations, an association between this increased risk and discrimination, and the potential benefit of cultivating spaces where individuals can be "out" in all aspects of themselves. CONCLUSIONS: There is a pressing need for research into interventions for LGBT populations with severe mental illness as well as descriptive studies to inform efforts to reduce illness morbidity linked to discrimination.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".