Past Disparities in Advance Care Planning Across Sociodemographic Characteristics and Cognition Levels in the United States
Bibliographic record
Abstract
We aimed to examine past advance care planning (ACP) in U.S. older adults across different sociodemographic characteristics and cognition levels. We established the baseline trends from 10 years ago to assess if trends in 2024 have improved upon future data availability. We considered two legal documents in the Health and Retirement Study 2014 survey as measures for ACP: a living will and durable power of attorney for healthcare (DPOAH). Logistic regression models were fitted with outcome variables (living will, DPOAH, and both) stratified by cognition levels (dementia/impaired cognition versus normal cognition). Predictor variables included age, gender, ethnicity, race, education, marital status, rurality, everyday discrimination, social support, and loneliness. Age, ethnicity, race, education, and rurality were significant predictors of ACP (having a living will, DPOAH, and both the living will and DPOAH) across cognition levels. Participants who were younger, Hispanic, Black, had lower levels of education, or resided in rural areas were less likely to complete ACP. Examining ACP and its linkages to specific social determinants is essential to understanding disparities and educational strategies needed to facilitate ACP uptake among different population groups. Accordingly, this study aimed to examine past ACP disparities in relation to specific social determinants of health and different cognition levels. Future studies are required to evaluate whether existing disparities have improved over the last 10 years when 2024 data is released. Addressing ACP disparities among diverse populations, including racial and ethnic minorities with reduced cognition levels, is crucial for enhancing health equity and access to care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".