The association between COVID-19 diagnosis or having symptoms and anxiety among Canadians: A repeated cross-sectional study
Bibliographic record
Abstract
Background The mental health effects of being diagnosed with COVID-19 are unknown. The present study examined whether individuals or those with someone close to them with a COVID-19 diagnosis differentially experienced anxiety during the pandemic.Methods Four web-based repeated cross-sectional surveys were conducted among Canadians aged 18 and older (n = 4015) regarding the impact of COVID-19 on mental health between May 8th and July 14th, 2020. Data on sociodemographic, COVID-19 symptoms/diagnoses for self or someone close, and anxiety were collected. Multiple logistic regression analyses were performed controlling for potential confounders.Results Anxiety among individuals affected by the pandemic remained stable over time. Individuals or those with someone close diagnosed with COVID-19 had greater odds of having anxiety (OR = 1.55; 95%CI 1.12, 2.14) compared to those who had not been diagnosed (self or close other) with COVID-19. Individuals or those with someone close to them who had symptoms of COVID-19 had greater odds of having anxiety (OR = 2.08; 95%CI 1.51, 2.87) compared to those who did not report symptoms (self or close other).Conclusions This evidence highlights the importance of targeted psychosocial interventions for those directly impacted by the COVID-19 virus.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".