Mental health and economic concerns from March to May during the COVID-19 pandemic in Canada: Insights from an analysis of repeated cross-sectional surveys
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic impacted the psychological wellbeing of populations worldwide. In this study, we assess changes in mental health during the early months of the pandemic in Canada and examine its relationship with another prominent problem during this time, economic concerns. METHODS: Analyses were based on two cycles of the nationally representative repeated cross-sectional Canadian Perspectives Survey Series (N=4627 in March and 4600 in May). We described the changes in mental health and economic concerns between March and May, and assessed the relationship between the two characteristics. RESULTS: Mental health declined significantly during the early months of the COVID-19 pandemic: the proportion of Canadian adults who reported only good/fair/poor mental health grew from 46% to 52% from March to May. Economic concerns including food insecurity were an important correlate of 'bad' mental health, as was younger age, female gender, and Canada-born status. Contrary to expectations, however, economic concerns lessened during this time frame. CONCLUSIONS: These findings suggest that policies to mitigate economic stress, such as Canada's Emergency Response Benefit, may have eased mental health deterioration in early pandemic months through a reduction in financial hardship. Interventions to increase the economic security of the population will have far-reaching consequences in terms of improved mental health, and should be continued throughout the 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".