Incident and Recurrent Depression among Adults Aged 50 Years and Older during the COVID-19 Pandemic: A Longitudinal Analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
The COVID-19 pandemic and accompanying public health measures have exacerbated many risk factors for depression in older adulthood. The objectives of the current study are: (1) to determine the risk of incident and recurrent depression during the COVID-19 pandemic among those with, or without, a history of depression; and (2) to identify factors that were predictive of depression in these two groups. The study population included 22,622 participants of the Canadian Longitudinal Study on Aging who provided data at baseline (2011-2015), follow-up (2015-2018), and twice during the pandemic (April-May 2020, September-December 2020). The Center for Epidemiologic Studies Depression Scale (CES-D-10) was used to classify individuals with depression. Logistic regression was used to estimate the odds of depression during COVID across a series of risk factors. Individuals with a history of depression had four times the risk of depression during the pandemic when compared to those without a history of depression, even after controlling for relevant covariates. Other factors associated with depression during the pandemic include being female, having fewer savings, and experiencing COVID-19 related stressors, such as health stressors, difficulties accessing resources, and family conflict. Clinicians working with older adults should consider interventions to support high-risk groups, such as those with recurrent depression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 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".