RACIAL AND ETHNIC VARIATIONS IN DEMENTIA DIAGNOSIS, SURVIVAL, AND END-OF-LIFE CARE QUALITY
Bibliographic record
Abstract
Abstract In the United States most adults have a preference to die at home and is an indicator of good end-of-life care. In the context of dementia, family members and caregivers are decision makers and part of good and equitable care involves understanding cultural variation in attitudes and social norms related to dementia, death and dying, and the meaning of a good death. This symposium explores racial and ethnic variation in lifetime dementia diagnosis and end-of-life care quality indicators. The first presentation examines racial, ethnic, and geographic variation in the rarely discussed lifetime prevalence of dementia and survival time from dementia diagnosis to death using national Medicare data. The second presentation describes the relationship between end-of-life care planning and satisfaction with end-of-life care using data from the Health and Retirement Study. The third presentation describes variation in place of death, a key indicator of end-of-life care quality, by dementia diagnosis and race/ethnicity using national Medicare data. The fourth presentation examines variation in hospice use, another indicator of end-of-life-care quality, and place of death by dementia diagnosis, race, and ethnicity using national Medicare data. The symposium concludes with a presentation examining the relationship between place of death and satisfaction with care received using data from the Health and Retirement Study. The Institute for Healthcare Improvement’s Triple Aim (improving the experience of care, improving the health of populations, and reducing per capita costs of health care) serves as a lens for discussing policy and practice implications of the major findings from each presentation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".