A rapid review of end-of-life needs in the LGBTQ+ community and recommendations for clinicians
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay, bisexual, transgender, and queer plus (LGBTQ+) adults face challenges accessing end-of-life care. Understanding the experiences of LGBTQ+ persons within the end-of-life context is crucial in addressing their needs and supporting equity at end of life. AIM: Review recent literature documenting the experiences of LGBTQ+ adults nearing end-of-life, identifying needs, barriers to care, and translating this into clinical recommendations. DESIGN: A rapid review design was chosen for prompt results. The process was streamlined by limiting the literature search to peer-reviewed articles, dissertations, theses, by date and language. Data collection used a predetermined set of items based on Meyer's Minority Stress and Bronfenbrenner's Ecological Models including participants' voices, needs, and barriers. Thematic analysis of collected data was conducted and presented results in a narrative summary. DATA SOURCES: We searched six electronic databases (PubMed, Medline, ProQuest Dissertations and Theses A&I, ProQuest Dissertations and Theses, Open Access Theses and Dissertations, CINAHL, and Google Scholar) for articles published from 2016 to 2020. RESULTS: We included and appraised for quality 33 articles. We uncovered three latent themes: systemic barriers, a lack of lived experience within the literature, and treatment of LGBTQ+ as one homogeneous group. CONCLUSIONS: The hybrid Meyer's Minority Stress and Bronfenbrenner Ecological model elucidated how stressors and social contexts may impact LGBTQ+ adults when accessing end-of-life care. Incorporating LGBTQ+ cultural competence training into continuing education and ensuring that LGBTQ+ individuals participate in the development of end-of-life care programming may better attend to the needs of 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 | 0.068 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.037 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".