Cross-National Analysis of Bereavement From COVID-19 and Depressive Symptoms Among Older Adults in Europe
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has left older adults around the globe grieving the sudden death of relatives and friends. We examine if COVID-19 bereavement corresponds with older adults’ depressive symptoms in 27 countries, and test for variation by gender and country context. We analyzed the Survey of Health, Ageing and Retirement in Europe (SHARE) COVID-19 data collected from N=51,383 older adults (age 50–104) living in 27 countries between June-August 2020, of whom 1,363 reported the death of a relative or friend from COVID-19. We estimated pooled-multilevel logistic regression models to examine if COVID-19 bereavement was associated with depressive symptoms and worsening depressive symptoms for older men and women, and we tested whether the national COVID-19 mortality rate in their country had an additive, or multiplicative, influence. COVID-19 bereavement from the death of a relative or friend is associated with significantly higher odds of reporting depressive symptoms, and reporting that these symptoms have recently worsened since the outbreak of COVID-19. Net of personal loss, living in a country with the highest COVID-19 mortality rate corresponds further with women’s depressive symptoms; however, living in the midst of more COVID-19 deaths does not alter the implications of personal loss for depressive symptoms. 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".