Advance care planning as perceived by marginalized populations: Willing to engage and facing obstacles
Bibliographic record
Abstract
BACKGROUND: Health disparities exist in end-of-life (EOL) care. Individuals and communities that are marginalized due to their race, ethnicity, income, geographic location, language, or cultural background experience systemic barriers to access and receive lower quality EOL care. Advance care planning (ACP) prepares patients and their caregivers for EOL decision-making for the purpose of promoting high-quality EOL care. Low engagement in ACP among marginalized populations is thought to have contributed to disparity in EOL care. To advance health equity and deliver care that aligns with the goals and values of each individual, there is a need to improve ACP for marginalized populations. AIM: To describe how patients from marginalized populations experience and perceive ACP. METHODS: We used an interpretive phenomenological approach with semi-structured qualitative interviews. Participants were recruited from four primary care clinics and one nursing home in a US Pacific Northwest city. Thirty patients from marginalized populations with serious illness participated in individual interviews between January and December 2021. Participants were asked to describe their experiences and perceptions about ACP during the interviews. RESULTS: The mean age of 30 participants was 69.5; 19 (63%) were women; 12 (40%) identified as Asian/Pacific Islanders, 10 (33%) as Black; and 9 (30%) were non-native English speakers. Our three key findings were: 1) patients from marginalized populations are willing to engage in ACP; 2) there were multiple obstacles to engaging in ACP; and 3) meaningful ACP conversations could happen when clinicians listen. Although participants from marginalized populations were willing to engage in ACP, a fragmented and restrictive healthcare system and clinicians' biased behaviors or lack of interest in knowing their patients were obstacles. Participants who felt their clinicians took time and listened were encouraged to engage in ACP. CONCLUSION: Patients from marginalized populations are willing to engage in ACP conversations despite a common belief otherwise. However, obstacles to meaningful ACP conversations with healthcare providers exist. Clinicians need to be aware of these obstacles and listen to build trust and engage marginalized patients in mutually meaningful ACP conversations.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".