Examining Regional Differences in Nursing Home Palliative Care for Black and Hispanic Residents
Bibliographic record
Abstract
Background: Approximately one-quarter of all deaths in the United States occur in nursing homes (NHs). Palliative care has the potential to improve NH end-of-life care, but more information is needed on the provision of palliative care in NHs serving Black and Hispanic residents. Objective: To determine whether palliative care services in United States NHs are associated with differences in the concentrations of Black and Hispanic residents, respectively, and the impact by region. Design: We conducted a cross-sectional analysis. The outcome was NH palliative care services (measured by an earlier national survey); total scores ranged from 0 to 100 (higher scores indicated more services). Other data included the Minimum Data Set and administrative data. The independent variables were concentration of Black and Hispanic residents (i.e., <3%, 3–10%, >10%), respectively, and models were stratified by region (i.e., Northeast, Midwest, South and West). We compared unadjusted, weighted mean palliative care services by the concentration of Black and Hispanic residents and computed NH-level multivariable linear regressions. Setting/Subjects: Eight hundred sixty-nine (weighted n = 15,020) NHs across the United States. Results: Multivariable analyses showed fewer palliative care services provided in NHs with greater concentrations of Black and Hispanic residents. Fewer palliative care services were reported in NHs in the Northeast, for which >10% of the resident population was Black, and NHs in the West for which >10% was Hispanic versus NHs with <3% of the population being Black and Hispanic (−13.7; p < 0.001 and −9.3; p < 0.05, respectively). Conclusion: We observed differences in NH palliative care by region and with greater concentration of Black and Hispanic residents. Our findings suggest that greater investment in NH palliative care services may be an important strategy to advance health equity in end-of-life care for Black and Hispanic residents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".