Pandemic-Induced Depression Among Older Adults with a History of Cancer During the COVID-19 Pandemic: Findings from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Purpose: The objectives of this study were to identify the prevalence of, and factors associated with, incident and recurrent depression in a sample of older adults with a history of cancer during the COVID-19 pandemic. Materials and Methods: Data were drawn from four waves of the Canadian Longitudinal Study on Aging Comprehensive Cohort (n=2486 with cancer). The outcome of interest was a positive screen for depression based on the CES-D-10 during the autumn of 2020. Results: Among older adults with cancer and no pre-pandemic history of depression (n=1765), 1 in 8 developed first onset depression during the pandemic. Among respondents with cancer and a history of depression (n=721), 1 in 2 experienced a recurrence of depression. The risk of both incident and recurrent depression was higher among those who were lonely, those with functional limitations, and those who experienced an increase in family conflict during the pandemic. The risk of incident depression only was higher among older women, those who did not engage in church or religious activities, those who experienced a loss of income during the pandemic, and those who became ill or had a loved one become ill or die during the pandemic. The risk of recurrent depression only was higher among those who felt isolated from others and those whose income did not satisfy their basic needs. Conclusion: Health care providers should continue to screen and provide mental health support to their cancer patients and those with a lifetime history of cancer, with consideration for those with the aforementioned vulnerabilities.
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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 | 0.003 |
| 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".