Depression during the COVID-19 pandemic among older Canadians with peptic ulcer disease: Analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
The COVID-19 pandemic and associated public health measures have exacerbated many known risk factors for depression that may be particularly concerning for individuals with chronic health conditions, such as peptic ulcer disease (PUD). In a large longitudinal sample of older adults with PUD, the current study examined the incidence of depression during the pandemic among those without a pre-pandemic history of depression (n = 689) and the recurrence of depression among those with a history of depression (n = 451). Data came from four waves of the Canadian Longitudinal Study on Aging (CLSA). Multivariate logistic regression analyses were conducted to identify factors associated with incident and recurrent depression. Among older adults with PUD and without a history of depression, approximately 1 in 8 (13.0%) developed depression for the first time during the COVID-19 pandemic. Among those with a history of depression, approximately 1 in 2 (46.6%) experienced depression during the pandemic. The risk of incident depression 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 women, individuals whose income did not satisfy their basic needs, those who were themselves ill and/or those whose loved ones were ill or died during the pandemic, and those who had disruptions to healthcare access during the pandemic. The risk of recurrent depression only was higher among those with chronic pain and those who had difficulty accessing medication during the pandemic. Implications for interventions are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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".