A Systematic Review of Guidelines for Emergency Department Care of Sexual Minorities: Implementable Actions to Improve Care
Bibliographic record
Abstract
INTRODUCTION: Sexual minorities, including lesbian, gay, bisexual, asexual, pansexual, and others make up 4.0-5.4% of the North American population. Stigmatization and minority stress can lead to poorer health status in sexual minorities, and a previous scoping review showed gaps in the emergency medicine (EM) literature for care of sexual minorities. In this review we sought to examine existing guidelines for the care of sexual minorities that made recommendations relevant to care in the emergency department (ED). METHODS: Using the PRISMA criteria, we performed a systematic search of OVID Medline, EMBASE, CINAHL, and the grey literature for clinical practice guidelines (CPG) and best practice statements (BPS) published until July 31, 2022. Articles were included if they were in English, included medical care of sexual minority populations of any age, in any setting, region, or nation, and were of national or international scope. Exclusion criteria included primary research studies, systematic or narrative reviews or otherwise non-CPG or BPS statements, editorials or letters to the editor, articles of regional or individual hospital scope, non-medical articles, or if a more recent version of the CPG or BPS existed. We identified, recorded, and assessed for quality all recommendations relevant to using the AGREE-II and AGREE-REX tools. Inter-rater reliability was assessed using the interclass correlation coefficient. We coded recommendations for the relevant point of care while in the ED (triage, registration, rooming, investigations, etc). RESULTS: We excluded 2,413 of 2,534 unique articles. Only nine articles contributed 11 ED-relevant recommendations. Seven of the nine articles were found to be of moderate to high quality; 6 of 11 recommendations were identified as high quality and adaptable. They included recommendations for screening, testing, and care of HIV+ sexual minority populations, and general or trauma care for men who have sex with men and sexual minority populations in general. CONCLUSION: This is the most comprehensive review of guidance documents for care of sexual minority populations to date. It identifies 11 actionable recommendations for the ED and identifies opportunities for community-led development of comprehensive clinical practice guidelines for care of sexual minority populations in the ED.
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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 | 0.084 | 0.297 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".