A portrait of the early and differential mental health impacts of the COVID-19 pandemic in Canada: Findings from the first wave of a nationally representative cross-sectional survey
Bibliographic record
Abstract
Evidence on the population-level mental health impacts of COVID-19 are beginning to amass; however, to date, there are significant gaps in our understandings of whose mental health is most impacted, how the pandemic is contributing to widening mental health inequities, and the coping strategies being used to sustain mental health. The first wave of a repeated cross-sectional monitoring survey was conducted between May 14-29, 2020 to assess the mental health impacts of the pandemic and to identify the disproportionate impacts on populations or groups identified as experiencing increased risks due to structural vulnerability and pre-existing health and social inequities. Respondents included a nationally representative probability sample (n = 3000) of Canadian adults 18 years and older. Overall, Canadian populations are experiencing a deterioration in mental health and coping due to the pandemic. Those who experience health, social, and/or structural vulnerabilities due to pre-existing mental health conditions, disability, income, ethnicity, sexuality, and/or gender are more likely to endorse mental health deterioration, challenging emotions, and difficulties coping. This monitoring study highlights the differential mental health impacts of the pandemic for those who experience health, social, and structural inequities. These data are critical to informing responsive, equity-oriented public health, and policy responses in real-time to protect and promote the mental health of those most at risk during the pandemic and beyond.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".