COVID-19-Related Changes in Assistance Networks for U.S. Older Adults with and without Dementia
Bibliographic record
Abstract
OBJECTIVES: Prepandemic research suggests assistance networks for older adults grow over time and are larger for those living with dementia. We examined how assistance networks of older adults changed in response to the onset of the coronavirus disease 2019 (COVID-19) pandemic and whether these changes differed for those with and without dementia. METHODS: We used 3 rounds of the National Health and Aging Trends Study. We estimated multinomial logistic regression models to test whether changes in assistance networks during COVID-19 (2019-2020)-defined as expansion, contraction, and adaptation-differed from changes prior to COVID-19 (2018-2019). We also estimated ordinary least squares regression models to test differences in the numbers of helpers assisting with one (specialist) versus multiple (generalist) domains before and during COVID-19. For both sets of outcomes, we investigated whether pandemic-related changes differed for those with and without dementia. RESULTS: Over all activity domains, a greater proportion of assistance networks adapted during COVID-19 compared to the pre-COVID-19 period (relative risk ratio = 1.19, p < .05). Contractions in networks occurred for those without dementia. Transportation assistance contracted for those with and without dementia, and mobility/self-care assistance contracted for those with dementia. The average number of generalist helpers decreased during COVID-19 (β = -0.09, p < .001). DISCUSSION: Early in the pandemic, assistance networks of older adults adapted by substituting helpers, by contracting to reduce exposures with more intimate tasks for recipients with dementia, and by reducing transportation assistance. Future research should explore the impact of such changes on the well-being of older adults and their assistance networks.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".