Disparities in Advance Care Planning Across Rurality, Sociodemographic Characteristics, and Cognition Levels: Evidence from the Health and Retirement Study
Bibliographic record
Abstract
Background: We aimed to examine ACP in older adults in the U.S. across different sociodemographic characteristics and cognition levels (N = 17,698). Methods: We utilized two legal documents from the Health and Retirement Study survey: a living will and durable power of attorney for healthcare (DPOAH). We established the baseline trends from 2014 to assess if trends in 2024 have improved upon future data availability. Logistic regression models were fitted with outcome variables (living will, DPOAH, and both) stratified by cognition levels (dementia/impaired cognition versus normal cognition). Results: 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, less educated, or resided in rural areas were less likely to complete ACP. Conclusion: Examining ACP and its linkages to specific social determinants is crucial for understanding disparities and developing effective educational and interventional strategies to enhance ACP uptake among diverse population groups. Future studies are needed to assess whether disparities have improved over the last decade, particularly as 2024 data become available. Addressing ACP disparities is essential for healthcare professionals to advance research and promote effective practices in geriatric care and aging services.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".