Bereavement From COVID-19, Gender, and Reports of Depression Among Older Adults in Europe
Bibliographic record
Abstract
OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has left older adults around the world bereaved by the sudden death of relatives and friends. We examine if COVID-19 bereavement corresponds with older adults' reporting depression in 27 countries and test for variations by gender and country context. METHOD: We analyze the Survey of Health, Ageing and Retirement in Europe COVID-19 data collected between June and August 2020 from 51,383 older adults (age 50-104) living in 27 countries, of whom 1,363 reported the death of a relative or friend from COVID-19. We estimate pooled multilevel logit regression models to examine if COVID-19 bereavement is associated with self-reported depression and worsening depression, and we test whether national COVID-19 mortality rates moderate these associations. RESULTS: COVID-19 bereavement is associated with significantly higher probabilities of both reporting depression and reporting worsened depression among older adults. Net of one's own personal loss, living in a country with the highest COVID-19 mortality rate is associated with women's reports of worsened depression but not men's. However, the country's COVID-19 mortality rate does not moderate associations between COVID-19 bereavement and depression. DISCUSSION: COVID-19 deaths have lingering mental health implications for surviving older adults. Even as the collective toll of the crisis is apparent, bereaved older adults 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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".