Is the Unequal COVID–19 Burden in Canada Due to Unequal Levels of Citizen Discipline across Provinces?
Bibliographic record
Abstract
L'inégalité des effets de la maladie du coronavirus 2019 (COVID-19) à travers les provinces canadiennes (notamment quant au taux d'infection et de décès) est importante et intrigante. Certains ont postulé que pour mieux comprendre les écarts dans les effets de la pandémie entre les provinces, il faudrait étudier les écarts dans l'application, par les citoyens, des mesures préventives de santé publique. Toutefois, aucun test empirique systématique n'a été effectué pour valider ce postulat. Dans cette recherche, nous utilisons un base de données de taille exceptionnelle, comprenant 23 vagues d'enquêtes (N= 22,610) réalisées sur 12 mois (avril 2020-avril 2021) pour répondre à la question « Existe-t-il une preuve de différences considérables entre les provinces dans l'application par les citoyens des mesures sanitaires de base en vue de prévenir la transmission de l'infection? » Nous constatons que les différences régionales dans le comportement autodéclaré sont très faibles, ce qui veut dire que l'écart des effets de la COVID-19 sur la santé entre les provinces n'avait pas grand-chose à voir avec l'application des mesures par les citoyens, du moins pendant la première année de la pandémie. Ces résultats ont des implications importantes. Même s'il est capital de continuer à étudier les variations régionales reliées au fléau de la COVID-19, les autorités publiques de la santé, les spécialistes et les politiciens, doivent être vigilants quand ils présentent l'application des mesures par les citoyens comme étant la première explication de l'écart des effets sur la santé entre les provinces. 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 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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".