Bibliographic record
Abstract
ABSTRACT Background The burden of COVID-19 in Canada is unequally distributed geographically, with the largest number of cases and fatalities recorded in Québec and Ontario while other provinces experienced limited outbreaks. To date, however, no study has assessed how provincial epidemics have unfolded in a comparative perspective. This is essential to calibrate projections of the future course of the epidemic and plan health care resources for the second wave of infections. Methods Using newly released individual-level data collected by the Public Health Agency of Canada, we assess COVID-19-related morbidity and mortality across age and gender groups at the provincial level through a combination of demographic and survival analyses. Results Québec has the highest absolute and per capita number of COVID-19 confirmed positive cases, hospitalizations and fatalities in all age groups. In each province, a higher number of women than men test positive for the disease, especially above age 80. Yet consistently across age groups, infected men are more likely to be hospitalized and enter intensive care than women do. These gender differences in hospitalisation rates account for the higher case fatality risk due to COVID-19 among men compared to women. Interpretation Although health care capacity across provinces has been sufficient to treat severe cases, we find that the main factor accounting for gender differences in COVID-19-related mortality is the need for hospitalization and intensive care, especially above age 80. This suggests a selection effect of severe cases requiring to be treated in a hospital setting that needs to be further investigated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".