COVID‐19 pandemic responses of Canada and United States in first 6 months: A comparative analysis
Bibliographic record
Abstract
INTRODUCTION: Canada and the United States have distinct health care and social policies, and it is important to see how they had been responding to the ongoing COVID-19 pandemic. METHODS: The study period was limited to the first 6 months of the pandemic and aimed to explore the responses by public health authorities, media, general population, and law makers during the initial phase of pandemic. RESULTS: Social disparity, underfunded pandemic preparation, and the initial failure to act appropriately have resulted in the rapid spread of infection in both countries. In the United States, prevailing social inequalities and racism, inaccessible health care, higher rates of preexisting medical conditions and disputed political leadership have further deteriorated the situation and enhanced public suffering, particularly for the black and Indigenous communities. In Canada, its poorly regulated services of long-term care facilities, initial restriction of testing and lack of access to epidemiological data have helped spread the infection and increased casualties in vulnerable populations. CONCLUSION: Analysis of the pandemic responses of the United States and Canada has revealed how existing social disparity, underfunded pandemic preparation, and the initial failure to act appropriately have resulted in the rapid spread of infection.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".