Equity Analysis of Repeated Cross-Sectional Survey Data on Mental Health Outcomes in Saskatchewan, Canada during COVID-19 Pandemic
Bibliographic record
Abstract
This paper aims to understand the impact of COVID-19 on three mental health outcomes-anxiety, depression, and mental health service use. Specifically, whether the associations between social and economic variables and these outcomes are exacerbated or buffered among equity-seeking groups in Saskatchewan. We analyzed secondary datasets of Saskatchewan adults from population-based national surveys conducted by Mental Health Research Canada (MHRC) on three occasions: cycle 2 (August 2020), cycle 5 (February 2021), and cycle 7 (June 2021). We examined temporal changes in the prevalence of anxiety, depression, and service utilization. Using the responses from 577 respondents in cycle 5 dataset (as it coincides with the peak of 2nd wave), we performed multinomial logistic regression. The policy implications of the findings were explored empirically through a World Café approach with 30 service providers, service users and policy makers in the province. The prevalence of anxiety and depression remained steady but high. Mental health services were not accessed by many who need it. Participants reporting moderate or severe anxiety were more likely to be 30-49 years old, women, and immigrants who earned less than $20,000 annually. Immigrants with either college or technical education presented with a lesser risk of severe anxiety. Factors associated with moderate or severe depression were younger age (<50 years), low household income, as well as immigrants with lower levels of education. Racialized groups had a lower risk of severe depression if they were under 30 years. Students and retirees also had a lower risk of severe depression. Canadian-born residents were more likely to require mental health supports but were not accessing them, compared to immigrants. Our analysis suggests mental health outcomes and service utilization remain a problem in Saskatchewan, especially among equity-seeking groups. This study should help drive mental health service redesign towards a client-centred, integrated, and equity-driven system in Saskatchewan.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".