A population-based repeated cross-sectional study using administrative health data to examine the impact of the COVID-19 pandemic on mental wellness in citizens of the Métis Nation of Ontario
Bibliographic record
Abstract
Objective and ApproachThe COVID-19 pandemic led to unprecedented levels of mental unwellness and yet there are no reports to date that share Métis-specific data. Our study examined changes in patterns of mental and addictions-related (MHA) outpatient health service utilization using population-based data on Métis Nation of Ontario (MNO) citizens before and during the COVID-19 pandemic. Administrative health data in Ontario, Canada between 2017 and 2022 was linked with the MNO's Citizenship Registry (2022) under an existing Data Governance and Sharing Agreement. Monthly rates of MHA outpatient visits were compared between the pre-COVID-19 period (March 2017 to February 2020) and post-COVID-19 onset (March 2020 to December 2022), and rate ratios comparing observed and expected rates were derived using Poisson generalized estimating equations. Stratifications by age and sex were examined. Results and ConclusionAmong 28,400 MNO citizens (50.3% male; median age 44 years; 28.3% living rurally), the average monthly rate of MHA outpatient visits was 47.4 per 1000 population pre-COVID-19. During the COVID-19 pandemic, the observed MHA outpatient visit rates were 13% higher than expected (RR=1.13; 95% CI: 1.02-1.26). MHA outpatient visits were higher in females compared to males and MNO citizens aged 30 to 64 years versus older or younger citizens. Implications This study is the first to use population-level study to examine MHA health service use in Métis people. High-quality, contemporary data on the mental health outcomes of MNO citizens is crucial to inform MNO’s Mental Health and Addictions programming, resource allocation and emergency preparedness.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".