Is the Unequal COVID–19 Burden in Canada Due to Unequal Levels of Citizen Discipline across Provinces?
Bibliographic record
Abstract
The unequal burden of the coronavirus disease 2019 (COVID-19) crisis (e.g., in terms of infection and death rates) across Canadian provinces is important and puzzling. Some have speculated that differences in levels of citizen compliance with public health preventive measures are central to understanding cross-provincial differences in pandemic-related health outcomes. However, no systematic empirical test of this hypothesis has been conducted. In this research, we make use of an exceptionally large dataset that includes 23 survey waves ( N = 22,610) fielded in Canada across 12 months (April 2020–April 2021) to answer the question “Is there evidence of substantial cross-provincial differences in citizen compliance with basic public health measures designed to prevent the spread of infection?” We find that regional differences in self-reported behaviour are few and very modest, suggesting that interprovincial differences in COVID-19–related health outcomes have little to do with differences in citizen compliance, at least in the first year of the pandemic. These results have important implications. Although it is crucial that we continue to study regional variations related to the COVID-19 burden, public health agency officials, pundits, and politicians should be cautious when musing about the role of citizen compliance as the primary explanation of interprovincial pandemic health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".