FACTORS ASSOCIATED WITH RECEIPT OF PAID CAREGIVING AMONG OLDER ADULTS WITH DEMENTIA
Bibliographic record
Abstract
While the demands of progressive dementia may overwhelm family caregivers and lead to the need for formal long-term care services, little is known about the rapidly growing number of paid caregivers (including home health aides, personal care workers, and other direct care workers) who step in to provide additional care to older adults with dementia in their homes. Using data from the nationally representative 2015 National Health and Aging Trends Study, we sought to 1) describe prevalence of paid caregiving and 2) examine factors associated with receipt of paid caregiving among older adults with dementia. Of the 899 sampled individuals with dementia, one quarter received paid care and approximately 10% received paid care for more than 20 hours per week. Multivariable analysis, which included sociodemographic characteristics as well as variables with significant univariate associations, found that those who received paid care were less likely to be female (OR 0.476, 0.297–0.762) and more likely to have Medicaid (OR 2.036, 1.169–3.545), live alone (OR 2.361, 1.301–4.284), and require help with a higher number of ADLs (OR 1.224, 1.080–1.388) and IADLs (1.42, 1.1216–1.617.) While we expected that those with more functional impairment would receive more paid care, these results point to the important role that contextual and social factors such as Medicaid coverage, gender, and household structure play in receipt of paid care. This information is essential to guide policy that meets the long-term care needs of a growing population of older adults with dementia who wish to remain living at home.
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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 | 0.006 |
| 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) | 0.002 | 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".