Mapping the Landscape of Those Left Behind When a Person With Dementia Dies
Bibliographic record
Abstract
INTRODUCTION: People with dementia can have many family and friends who might be affected by their deaths. Pursuing the long-term aim of understanding how dementia deaths affect close family and friends, this project lays groundwork through estimates of who those close family and friends are, with special attention to race and ethnicity. METHOD: Regression models estimated associations between dementia, race/ethnicity, and close family and friend network size, controlling for age, sex, education, marital status, and household wealth for 1386 deceased people with dementia from the Health and Retirement Study (2004 to 2018). RESULTS: Persons with dementia had an average of 9.4 close family and friends at death. But patterns of close family and friends were different among non-Latino Black (10.8), Latino (9.9), and non-Latino White (9.2) people with dementia at death. Notably, non-Latino White persons with dementia had the fewest close family (3.7), followed by non-Latino Black (5.1), and Latino (7.7) persons with dementia. DISCUSSION: Knowing who might be affected by dementia deaths is the first step to explore how dementia-related deaths impact close family and friends. Future work can now sample bereaved family and friends of people with dementia to explore their experiences and develop culturally appropriate supports.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".