COVID-19 WIDOWHOOD OR PANDEMIC WIDOWHOOD: EXAMINING THE DIFFERENTIAL IMPLICATIONS FOR MENTAL HEALTH
Bibliographic record
Abstract
Abstract Millions of COVID-19 widows worldwide face elevated mental health risks that foreshadow worsening physical health and elevated mortality. It remains unknown whether the excess mental health problems for COVID-19 widows are a result of the “bad death” experiences from COVID-19 (e.g., unexpected death and high levels of medical intervention) or pandemic-induced social changes (e.g., social isolation and limited funerals). This study examines whether older adults whose spouses died of COVID-19 disease have worse mental health (self-reported depression, loneliness, and trouble sleeping) than those whose spouses died from causes other than COVID-19 before and during the pandemic. We used Survey of Health, Ageing and Retirement in Europe data collected before (Wave 8, fielded October 2019 to March 2020) and during the pandemic (COVID-19 Supplement-2, fielded June to August 2021) to compare three groups whose spouses died (a) before the pandemic, (b) from COVID-19 during the pandemic, and (c) from non-COVID-19 causes during the pandemic. We find those spouses died from COVID-19 have higher risks of self-reported depression, loneliness, and trouble sleeping than those losing a spouse before the pandemic. However, losing a spouse due to non-COVID-19 causes during the pandemic is not significantly associated with worse mental health compared to pre-pandemic scenarios. During the pandemic, older adults whose spouses died from COVID-19 report higher risks of loneliness than those spouses died from non-COVID-19 causes. This study suggests losing a spouse due to COVID-19 presents unique mental health risks for older adults, clarifying prior theories about mental health impacts of pandemic bereavement.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".