End-of-Life Preparations among Lesbian, Gay, Bisexual, and Transgender People: Integrative Review of Prevalent Behaviors
Bibliographic record
Abstract
Proactively making end-of-life (EOL) preparations is important to ensure high quality EOL care. Critical to preparation is the discussion of preferences with one's primary health care providers. Lesbian, gay, bisexual, and transgender (LGBT) people often experience discrimination from health care providers that will detrimentally affect their ability to communicate their care preferences. Structural barriers, such as those based on sexual orientation and gender identity, may impede timely and quality care when one is most in need. The aim of this study was to examine the prevalence of EOL preparatory behaviors among LGBT people, with particular focus on transgender individuals. Eight survey instruments with 30 prevalence estimates found in the literature were analyzed. EOL discussions between LGBT people and their primary health care providers were rare (10%). Transgender people were found to be even less prepared for EOL; they were 50-70% less likely than their LGB counterparts to have a will, a living will or to have appointed a healthcare proxy. A need exists for future mixed-methods research focused on LGBT populations accompanied by the cultural sensitivity needed to ensure their wishes are honored at the EOL.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".