The Experience of Black Patients With Serious Illness in the United States: A Scoping Review
Bibliographic record
Abstract
CONTEXT: Black patients experience health disparities in access and quality of care. OBJECTIVE: To identify and characterize the literature on the experiences of Black patients with serious illness across multiple domains - physical, spiritual, emotional, cultural, and healthcare utilization. METHODS: We conducted a scoping review of US literature from the last ten years using the PRISMA-ScR framework. PubMed was used to conduct a comprehensive search, followed by recursive citation searches in Scopus. Two reviewers screened the resulting citations to determine eligibility for inclusion and extracted data, including study methods and sample populations. The included articles were categorized by topic and then further organized using the Social-Ecological Model. RESULTS: From an initial review of 433 articles, a final sample of 160 were included in the scoping review. The majority of articles used quantitative research methods and were published in the last four years. Articles were categorized into 20 topics, ranging from Access to Hospice and Utilization (42 articles) to Community Outreach and Services (three articles). Three-quarters (76.3%) of the included studies provided evidence that racial disparities exist in serious illness care, while less than one-quarter examined causes of disparities. The most common Model levels were the Health Care System (102 articles) and Individual (71 articles) levels. CONCLUSION: More articles focused on establishing evidence of disparities between Black and White patients than on understanding their root causes. Further investigation is warranted to understand how factors at the patient, provider, health system, and society levels interact to remediate disparities.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".