The COVID-19 pandemic in Canada’s provinces: mitigation measures and outcomes
Bibliographic record
Abstract
Abstract The rapid spread of SARS-CoV-2 in early 2020 forced provincial health authorities across Canada to quickly institute infection control measures. It is now four years since the start of the global pandemic, and an opportune time to consider how Canada’s provinces compared in their SARS-CoV-2 containment policies and the resulting impacts on mortality and economic activity. I compare provincial exposure to SARS-CoV-2 using data on the number out-of-province arrivals into each province. I compare the key containment measures used in each province, the length of time that these measures were imposed, and the uptake of COVID-19 vaccines by province. Using Statistics Canada data, I also estimate the impact of the COVID-19 pandemic on provincial crude death rates during 2020-2023, and life expectancy and gross domestic product during 2020-2022. I find substantial provincial variation in pandemic responses and outcomes. The provinces varied in their use of the most stringent public health measures. Uptake of the primary COVID vaccinations varied from 76% to 92%; booster vaccination uptake varied even more. There was also marked provincial variation in mortality and economic outcomes. While this study does not estimate the impacts of provincial response stringency and COVID vaccine uptake on mortality and other outcomes, it does provide suggestive evidence that can be formally assessed in future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".