Psychological Impact of COVID-19 on People with Pre-Existing Chronic Disease
Bibliographic record
Abstract
The COVID-19 pandemic has caused an increase in anxiety and depression levels across broad populations. While anyone can be infected by the virus, the presence of certain chronic diseases has been shown to exacerbate the severity of the infection. There is a likelihood that knowledge of this information may lead to negative psychological impacts among people with chronic illness. We hypothesized that the pandemic has resulted in increased levels of anxiety and depression symptoms among people with chronic illness. We recruited 540 participants from the ongoing Prospective Urban and Rural Epidemiology (PURE) study in British Columbia, Canada. Participants were asked to fill out an online survey that included the Hospital Anxiety Depression Scale (HADS) to assess anxiety and depression symptoms. We tested our hypothesis using bivariate and multivariable linear regression models. Out of 540 participants, 15% showed symptoms of anxiety and 17% reported symptoms of depression. We found no significant associations between having a pre-existing chronic illness and reporting higher levels of anxiety or depression symptoms during COVID-19. Our results do not support the hypothesis that having a chronic illness is associated with greater anxiety or depression symptoms during the COVID-19 pandemic. Our results were similar to one study but in contrast with other studies that found a positive association between the presence of chronic illness and developing anxiety or depression during this 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".