End‐of‐Life Preferences: How Do They Differ Between Individuals with Poor and Normal Cognition?
Bibliographic record
Abstract
BACKGROUND: Communicating end-of-life (EOL) preferences is essential to ensure that care aligns with individuals' values and priorities, particularly for those with cognitive impairments who may face unique challenges in expressing their wishes. However, little is known about how EOL preferences differ between individuals with different cognition levels, particularly across diverse populations. This study examines these differences, considering variations in race/ethnicity and socioeconomic status. METHOD: Public-use data from the exit files of the Health and Retirement Survey (HRS,1995-2016) were analyzed. Exit files include participants who died since the previous HRS wave and had a proxy respondent who completed an interview. Cognition status was determined based on the proxy's evaluation of the deceased individual's memory one month before death, ranked on a Likert scale from poor to excellent. Descriptive statistics were calculated, and chi-square tests were used to assess significant differences between groups. RESULT: The decedents' impaired cognition group was significantly more likely to be single, less educated, white, and female. They were also significantly more likely to have a living will compared to those with normal cognition (46% vs. 42%, p <0.01). Conversely, a higher proportion of individuals with normal cognition had discussed EOL care compared to those with impaired cognition (56% vs. 51%, p <0.01). No significant differences by cognition status were found for family refusal of treatment or preferences for withholding treatment at EOL. However, significantly fewer participants with impaired cognition expressed a preference for receiving all care (29% vs. 33%, p <0.01), while a significantly higher proportion preferred to be made comfortable compared to those with normal cognition (95% vs. 92%, p <0.01). Interestingly, no significant differences were observed between the groups regarding whether cost influenced their EOL decisions. CONCLUSION: Individuals with impaired cognition may differ from those with normal cognition in their end-of-life preferences, particularly in their likelihood of having a living will, discussing EOL care, and preferring comfort-focused care over life-prolonging measures, highlighting the need for targeted interventions to address cognitive and sociodemographic disparities in advance care planning.
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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 | 0.002 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".