Gender Differences in Mental Health Symptoms Among Canadian Older Adults During the COVID-19 Pandemic: a Cross-Sectional Survey
Bibliographic record
Abstract
Background Older women’s mental health may be disproportionally affected by the COVID-19 pandemic due to differences in gender roles and living circumstances associating with aging. Methods We administered an online cross-sectional nationwide survey between May 1st and June 30th, 2020 to a convenience sample of older adults aged ≥55 years. Our outcomes were symptoms of depression, anxiety, and loneliness measured by three standardized scales: the eight-item Center for Epidemiological Studies Depression Scale, the five-item Beck Anxiety Inventory, and the Three-Item Loneliness Scale. Multivariable logistic regression was used to compare the odds of depression, anxiety and loneliness between men and women, adjusting for relevant confounders. Results There were 1,541 respondents (67.8% women, mean age 69.3 ± 7.8). 23.3% reported symptoms of depression (29.4% women, 17.0% men), 23.2% reported symptoms of anxiety (26.0% women, 19.0% men), and 28.0% were lonely (31.5% women, 20.9% men). After adjustment for confounders, the odds of reporting depressive symptoms were 2.07 times higher in women compared to men (OR 2.07 [95%CI 1.50–2.87] p < .0001). The odds of reporting anxiety and loneliness were also higher. Conclusions Older women had twice the odds of reporting depressive symptoms compared to men, an important mental health need that should be considered as the COVID-19 pandemic unfolds.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".