Anxiety and depression in Canada during the COVID-19 pandemic: A national survey.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it