Breathless and Blue in the Canadian Longitudinal Study on Aging: Incident and Recurrent Depression Among Older Adults with COPD During the COVID-19 Pandemic
Bibliographic record
Abstract
Background and Objectives: The COVID-19 pandemic and related public health measures intensified risk factors for depression and concurrently heightened numerous health-related stressors for individuals with Chronic Obstructive Pulmonary Disease (COPD). Utilizing a comprehensive longitudinal sample of Canadian older adults, this study examined the incidence and recurrence of depression among older adults with COPD, and identified factors that were associated with depression during the pandemic among this population. Methods: Data came from four phases of the Canadian Longitudinal Study on Aging (CLSA) (n=875 with COPD). The primary outcome of interest was a positive screen for depression based on the CES-D-10, during autumn of 2020. Bivariate and multivariate logistic regression analyses were performed to identify factors that were associated with depression. Results: Approximately 1 in 6 (17%) respondents with COPD and no lifetime history of depression developed depression for the first time during the early stages of the pandemic. Approximately 1 in 2 (52%) participants with COPD and a history of depression experienced a recurrence of depressive symptoms during this period. Loneliness, functional limitations, and family conflict were associated with a higher risk of both incident and recurrent depression. The risk of incident depression only was higher among those who had difficulty accessing healthcare resources. The risk of recurrent depression only was higher among women, those with a post-secondary education, and those with more adverse childhood experiences. Conclusion: Screening and interventions aimed at individuals with COPD, both with and without a history of depression, are warranted to potentially mitigate the mental health impacts of the COVID-19 pandemic.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".