Changes in late‐life assistance networks for Black and White older adults during the <scp>COVID</scp> ‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has disproportionately impacted older Black Americans. Given that assistance networks play a crucial role in older adults' ability to respond to challenges, we sought to investigate whether older adults' assistance network size changed during the COVID-19 pandemic and differed by race. METHODS: We analyzed data from the 2018-2020 rounds of the U.S. National Health and Aging Trends Study for Black and White adults aged 70 and older receiving help in the community or residential care settings. We used ordinary least squares regression to compare changes in assistance network size in the 2 years pre-COVID-19 (2018-2019, N = 3438) to changes in size at the onset of COVID-19 (2019-2020, N = 3185). RESULTS: Black older adults had larger assistance networks with a greater number of family helpers before and during the pandemic compared to their White counterparts. Assistance network size for older adults increased before but not during the pandemic mostly due to declines in unpaid nonrelative helpers and lack of increase in paid helpers. These effects did not differ by race. CONCLUSIONS: Black and White older adults experienced similarly sized reductions in their assistance networks as a consequence of the COVID-19 pandemic. Future research should investigate the relationship between these network changes and the unmet needs of older adults.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".