Predictors of persistent depressive and anxiety symptoms among older adults during the <scp>COVID</scp> ‐19 pandemic in Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has increased the risk of psychological distress for older adults during a period of reduced access to mental health support. We identified predictors of persistent depressive and anxiety symptoms among older adults throughout the initial 9 months of the pandemic. METHODS: This was a longitudinal study using web-based surveys sent to a convenience sample of Canadians aged ≥55 years. A baseline survey was sent in May 2020, with eight surveys sent monthly between June 2020 and January 2021. Data were separated into three study periods, corresponding to coronavirus case rates. Respondents who met predetermined thresholds for elevated depressive or anxiety symptoms using standardized mental health scales during all three periods were defined as having persistent symptoms. Multivariable Poisson regression was used to compare the risk of persistent symptoms across respondent characteristics selected a priori. RESULTS: There were 835 respondents who answered questions about depressive symptoms and 865 who answered questions about anxiety symptoms across all three study periods. Among those with complete follow-up data, 19.2% (n = 159/827) reported persistent depressive symptoms and 16.4% (n = 140/856) reported persistent anxiety symptoms. Respondents with persistent depressive symptoms were more likely to be women (adjusted relative risk [aRR] 1.71; 95% CI 1.19-2.46), and report multimorbidity (aRR 1.45; 95% CI 1.07-1.97), pre-existing depression (aRR 2.00; 95% CI 1.45-2.77) and pre-existing anxiety (aRR 1.50; 95% CI 1.10-2.06). The same respondent characteristics were correlates of persistently elevated anxiety symptoms. CONCLUSIONS: Older women, older adults with pre-existing depression and anxiety, and those with multimorbidity were more likely to report persistent mental health symptoms during the first 9 months of the COVID-19 pandemic. These individuals were known to have elevated risk prior to the pandemic, and expanded interventions targeted to these groups are necessary to adequately address their mental health needs.
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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 | 0.001 |
| 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".