LEFT OUT TO DRY: UNMET NEEDS AND RISK OF DEPRESSION AMONG OLDER ADULTS
Bibliographic record
Abstract
Abstract Pre-pandemic research has shown adverse consequences of having unmet care needs for older adults’ mental health. Due to the broad psychological distress and increased caregiving challenges during COVID-19, older adults’ vulnerabilities to unmet needs may be amplified by the pandemic, especially for those with functional limitations and intense care needs. This study aims to examine (1) the associations between unmet needs and depression among older adults before and during the COVID-19 and (2) whether the excess mental health consequences from unmet needs and COVID-19 vary by older adults’ dementia status. We pool data from the 2018, 2019, and 2020 rounds of National Health and Aging Trends Study, a nationally representative sample of U.S. Medicare beneficiaries. We analyze N=6,273 older adults aged 70 years and older who had limitations with self-care, household activities, or mobility. Results show that older adults with functional limitations experienced increased risk of depression over time. Before and during the pandemic, older adults with unmet needs and older adults with probable dementia had higher risks of depression compared to their counterparts, respectively. The risk of depression was highest among older adults who had probable dementia and could not have their care needs met. For older adults without dementia, their risks of depression increased significantly from pre-pandemic to COVID-19 if they had unmet care needs. Findings demonstrate the disproportionate impacts of COVID-19 on mental health among older adults. Older adults who have cognitive impairments and unmet needs are in particular need of mental health support.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".