Anxiety and depression in Canada during the COVID-19 pandemic: A national survey.
Bibliographic record
Abstract
Depression and anxiety are the most prevalent mental health problems in Canada. The COVID-19 pandemic will likely result in a large increase in the incidence and prevalence of anxiety and depression and experts are already warning of an “echo pandemic” of mental health problems. The objective is this research was to explorehowCanadiansaremanagingwiththeCOVID-19outbreakanddeterminetheimpactofthepandemic on levels of anxiety and depression. A nationally representative sample of 1,803 participants completed an online survey that was offered in both official languages. The percentage of respondents who indicated that their anxiety was high to extremely high quadrupled (from 5% to 20%) and the number of participants with high self-reported depression more than doubled (from 4% to 10%) since the onset of COVID-19. Although current anxiety levels are expected to remain the same, respondents predicted that depression would worsen if physical distancing and self-isolation continue for another 2 months. One-third of Canadians with anxiety and depression also report an increase in alcohol and cannabis use during the pandemic. Canadians with depression and anxiety also indicate that the quantity and quality of mental health support systems has decreased. Finally, a sizable proportion of Canadians believe that the federal and provincial governments should do more to support the mental health of Canadians. Recommendations for psychologists responding to mental health needs during and following the pandemic are provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".