End-of-life care in nursing home settings: Do race or age matter?
Bibliographic record
Abstract
OBJECTIVE: One-quarter of all U.S. chronic-disease deaths occur in nursing homes, yet few studies examine palliative care quality in these settings. This study tests whether racial and/or age-based differences in end-of-life care exist in these institutional settings. METHODS: We abstracted residents' charts (N = 1133) in 12 nursing homes. Researchers collected data on indicators of palliative care in two domains of care--advance care planning and pain management--and on residents' demographic and health status variables. Analyses tested for differences by race and age. RESULTS: White residents were more likely than minorities to have DNR orders (69.5% vs. 37.3%), living wills (39% vs. 5%), and health care proxies (36.2% vs. 11.8%; p < .001 for each). Advance directives were highly and positively correlated with age. In-depth advance care planning discussions between residents, families, and health care providers were rare for all residents, irrespective of demographic characteristics. Nursing staff considered older residents to have milder and less frequent pain than younger residents. We found no disparities in pain management based on race. SIGNIFICANCE OF RESULTS: To the extent that advance care planning improves care at the end of life, racial minorities in nursing homes are disadvantaged compared to their white fellow residents. Focusing on in-depth discussions of values and goals of care can improve palliative care for all residents and may help to ameliorate racial disparities in end-of-life care. Staff should consider residents of all ages as appropriate recipients of advance care planning efforts and should be cognizant of the fact that individuals of all ages can experience pain. Nursing homes may do a better job than other health care institutions in eliminating racial disparities in pain management.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".