Depression during the COVID‐19 pandemic among older adults with stroke history: Findings from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic and accompanying public health measures exacerbated many known risk factors for depression, while also increasing numerous health-related stressors for people with stroke history. Using a large longitudinal sample of older adults, the current study examined the prevalence of incident and recurrent depression among participants with stroke history, and also identified factors that were associated with depression during the pandemic among this population. METHODS: Data came from four waves of the Canadian Longitudinal Study on Aging's (CLSA) comprehensive cohort (n = 577 with stroke history; 46.1% female; 20.8% immigrants; mean age = 74.56 SD = 9.19). The outcome of interest was a positive screen for depression, based on the CES-D-10, collected during the 2020 CLSA COVID autumn questionnaire. Bivariate and multivariate logistic regression analyses were conducted to identify factors that were associated with depression. RESULTS: Approximately 1 in 2 (49.5%) participants with stroke history and a history of depression experienced a recurrence of depression early in the pandemic. Among those without a history of depression, approximately 1 in 7 (15.0%) developed depression for the first time during this period. The risk of depression was higher among immigrants, those who were lonely, those with functional limitations, and those who experienced COVID-19 related stressors, such as increased family issues, difficulty accessing healthcare, and becoming ill or having a loved one become ill or die during the pandemic. CONCLUSIONS: Interventions that target those with stroke history, both with and without a history of depression, are needed to buffer against the stressors of the COVID-19 pandemic and support the mental health of this population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".