ADVANCE CARE PLANNING IN DEMENTIA VERSUS NORMAL COGNITION: A DEMOGRAPHIC AND PSYCHOSOCIAL STUDY
Bibliographic record
Abstract
Abstract Older adults from minority groups often face higher rates of chronic diseases and cognitive impairment, along with lower rates of advance care planning and comfort care at end-of-life. This study aimed to explore patterns of advance care planning among older adults in the U.S., focusing on various demographic and psychosocial factors and cognition levels. We utilized data from the 2018 Health and Retirement Study to investigate the association between these factors and the likelihood of having a living will, a durable power of attorney for healthcare (DPOAH), or both, stratified by cognition (dementia/impaired cognition vs. normal cognition). Variables such as race, ethnicity, rurality, marital status, gender, education, age, and discrimination were included in the models. Among the 17,698 respondents, 71.6% exhibited normal cognition, while 28.4% had been diagnosed with dementia or impaired cognition. In both cognition groups, Black and Hispanic participants, as well as younger individuals with lower educational attainment, were less likely to have established a living will. Hispanic participants and younger individuals with lower educational attainment were also less likely to have a DPOAH and both a DPOAH and living will. Significant predictors of having a living will, DPOAH, or both that were not consistent among groups included rurality, marital status, and experiences of everyday discrimination. These findings underscore the importance of addressing disparities in advance care planning among older adults with diverse identities and levels of cognition. Understanding these differences is crucial for designing advance care planning for a diverse aging population facing health and cognitive decline.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".