FAMILIES AND DEMENTIA: ESTIMATES AND EXPOSURES
Bibliographic record
Abstract
Abstract Roughly 10 to 20% of older adults in the U.S. have dementia, which could have implications for their families and household members, who are likely to be called upon to provide care. Yet, it is not clear how many families and households include someone with dementia. This study provides national level estimates of families and households with an older adult with possible dementia using the 2017 Panel Study of Income Dynamics (PSID). Consistent with prior work, we find that 21.5% of older adults 65+ have possible dementia. Moreover, more than a quarter (26.3%) of households with an adult aged 65+ include an older adult with dementia, and 37.0% of extended family networks (with at least one adult aged 65+) have at least one older adult with dementia. Individual-level stratification of dementia by racial-ethnic group and socioeconomic status translates directly to household- and family-level patterns. For instance, households and families with an older adult who is either Black or Hispanic or does not hold a college degree are more likely to include at least one person with possible dementia. These findings have implications for the extent to which American families and households include someone with dementia, which comes with a risk of serving as a caregiver as well as possible implications for health and well-being. Our findings also establish important baseline estimates that can be measured against future estimates, for instance those post COVID-19.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".