Adherence to End-of-Life Instructions Across Cognition Levels: Insights from the Health and Retirement Study
Bibliographic record
Abstract
Abstract Adherence to end-of-life instructions is understudied and may vary across cognition levels in older adults, as cognitive decline can impact decision-making and the implementation of advance care planning. This study aims to examine adherence to end-of-life instructions across different cognition levels in older adults. Data from deceased participants in the Health and Retirement Study (HRS, 1994–2018) were extracted using exit files to assess adherence to end-of-life instructions. Langa Weir classification was used for cognition categorization. Adherence was measured using three indicators: proxy-reported difficulties in following written EOL instructions, proxy-reported healthcare provider difficulties in following written instructions, and instances where the patient received unwanted treatment despite family or decision-maker refusal. Individuals with dementia and impaired cognition were older at death, less likely to be married, more likely to have a high school education or less, and predominantly female. They were less likely to discuss end-of-life care prior to death or have a living will at death (p < 0.05) and marginally less likely to want all possible care (p = 0.06). Regarding adherence indicators, dementia group participants were 2.37 times more likely to receive unwanted treatment despite family/decision-maker refusal compared to normal cognition participants (OR = 2.37 [95% CI: 1.04–5.40], p < 0.05). Our findings highlight disparities in adherence to end-of-life instructions, with individuals with dementia being more likely to receive unwanted treatment. These results underscore the need for improved communication between individuals involved in end-of-life care and strategies to ensure that end-of-life preferences are honored across cognition levels.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".