Mental Health Inequities Amid the COVID-19 Pandemic: Findings From Three Rounds of a Cross-Sectional Monitoring Survey of Canadian Adults
Bibliographic record
Abstract
Objectives: Adverse mental health impacts of the COVID-19 pandemic are well documented; however, there remains limited data detailing trends in mental health at different points in time and across population sub-groups most impacted. This paper draws on data from three rounds of a nationally representative cross-sectional monitoring survey to characterize the mental health impacts of COVID-19 on adults living in Canada (N = 9,061). Methods: Descriptive statistics were used to examine the mental health impacts of the pandemic using a range of self-reported measures. Multivariate logistic regression models were then used to quantify the independent risks of experiencing adverse mental health outcomes for priority population sub-groups, adjusting for age, gender, and survey round. Results: Data illustrate significant disparities in the mental health consequences of the pandemic, with inequitable impacts for sub-groups who experience structural vulnerability related to pre-existing mental health conditions, disability, LGBTQ2+ identity, and Indigenous identity. Conclusion: There is immediate need for population-based approaches to support mental health in Canada and globally. Approaches should attend to the root causes of mental health inequities through promotion and prevention, in addition to treatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".