Providing Palliative Care for Sexual and Gender Minority Individuals: A Qualitative Interview Study of Physicians’ Attitudes and Experiences
Bibliographic record
Abstract
Introduction:Sexual and gender minority (SGM) individuals face increased risk of receiving suboptimal care, including palliative care. Despite research demonstrating strategies to improve care, little is known about the experiences of palliative care clinicians providing care to these communities. Objectives:The primary aim of this study is to characterize attitudes and practices of palliative care physicians around providing care to SGM individuals. Design:This exploratory, qualitative study used semi-structured interviewing. Interviews were transcribed and coded using reflexive thematic analysis. Setting and Participants:Twenty-four palliative care physicians practicing in the homecare, hospice, and hospital settings from geographically diverse sites across Canada were recruited from palliative care organizations using convenience and snowball sampling. Results:Four main themes represent perspectives on improving palliative care for SGM individuals: (1) increasing experience with and knowledge about SGM communities increases clinicians’ confidence and competency; (2) standardizing inclusive sexual orientation and gender identity (SOGI) data collection and documentation can improve patient care; (3) addressing individual, systemic, and societal biases may improve palliative care provided to SGM individuals; and (4) knowing SOGI improves care quality. Conclusions:Clinicians must familiarize themselves with the importance of SOGI to the care provided as well as the palliative care needs of SGM communities. Institutions should provide tailored training around the unique needs of SGM patients and implement policies and tools that standardize sexual and gender orientation data collection and documentation.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".