Healthcare and End-of-Life Needs of Lesbian, Gay, Bisexual, and Transgender (LGBT) Older Adults: A Scoping Review
Bibliographic record
Abstract
Lesbian, gay, bisexual, and transgender (LGBT) older adults face a number of challenges with respect to access to healthcare especially towards end-of-life. Through a systematic search and scoping review of the literature, we sought to answer two related research questions. In particular, the purpose of this scoping review was to determine the healthcare needs of LGBT older adults nearing end-of-life as well as the factors that contribute to a good death experience among older adults who identify as LGBT. A systematic search of electronic databases for articles published between 2005 and 2016 as well as screening for relevance resulted in 25 results. The data were charted and grouped according to the themes of: social support and chosen family, intimacy, health status, fear of discrimination and lack of trust, lack of knowledge and preparedness, and cultural competence in the healthcare system. The results suggest a role for health and social service workers in contributing to a positive care experience for LGBT older adults by becoming knowledgeable about the unique needs of this population and being unassuming and accepting of individuals' sexuality. Many of the articles reviewed collected data outside of Canada, limiting generalizability and highlighting a need for Canadian data on LGBT aging and end-of-life.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".