Worries, attitudes, and mental health of older adults during the <scp>COVID</scp> ‐19 pandemic: Canadian and <scp>U.S.</scp> perspectives
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Differences in older adults' worry, attitudes, and mental health between high-income countries with diverging pandemic responses are largely unknown. We compared COVID-19 worry, attitudes towards governmental responses, and self-reported mental health symptoms among adults aged ≥55 in the United States and Canada early in the COVID-19 pandemic. DESIGN: Online cross-sectional survey administered between April 2nd and May 31st in the United States and between May 1st and June 30th, 2020 in Canada. SETTING: Nationally in the United States and Canada. PARTICIPANTS: Convenience sample of older adults aged ≥55. MEASUREMENTS: Likert-type scales measured COVID-19 worry and attitudes towards government support. Three standardized scales assessed mental health symptoms: the eight-item Center for Epidemiological Studies Depression Scale, the five-item Beck Anxiety Inventory, and the three-item UCLA loneliness scale. RESULTS: There were 4453 U.S. respondents (71.7% women; mean age 67.5) and 1549 Canadian (67.6% women; mean age 69.3). More U.S. respondents (71%) were moderately or extremely worried about the pandemic, compared to 52% in Canada. Just 20% of U.S. respondents agreed or strongly agreed that the federal government cared about older adults in their COVID-19 pandemic response, compared to nearly two-thirds of Canadians (63%). U.S. respondents were more likely to report elevated depressive and anxiety symptoms compared to Canadians; 34.2% (32.8-35.6) versus 25.6% (23.3-27.8) for depressive and 30.8% (29.5-32.2) versus 23.7% (21.6-25.9) for anxiety symptoms. The proportion of United States and Canadian respondents who reported loneliness was similar. A greater proportion of women compared to men reported symptoms of depression and anxiety across all age groups in both countries. CONCLUSION: U.S. older adults felt less supported by their federal government and had elevated depressive and anxiety symptoms compared to older adults in Canada during early months of the COVID-19 pandemic. Public health messaging from governments should be clear, consistent, and incorporate support for mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".