The Effectiveness of Government Responses to the Coronavirus: A Comparative Analysis of The United States and Canada
Bibliographic record
Abstract
Abstract: This paper provides a detailed comparative analysis of the United States and Canada in terms of which country had a more effective response to the coronavirus pandemic. To address this question, several factors are considered: population distributions and demographic compositions, political leadership, cultural differences, health care systems, and vaccine production. Each of these variables had an impact on infection and death rates in each country. Canada was better equipped than the United States to respond to the pandemic in 4 out of the 5 categories listed above: 1) Population distributions were in Canada’s favor because American cities are more densely populated, and they were hit first by the pandemic. New York and Seattle were initial hotspots for the coronavirus, and these areas struggled to control infection and death rates in the early stages of the pandemic. Canada had the advantage of being hit afterward and benefitted from the proactive border-closure policy. 2) Prime Minister Justin Trudeau displayed stronger political leadership than President Donald Trump. President Trump frequently downplayed the threat of the coronavirus and he failed to cooperate with safety measures put forward by public health agencies. Moreover, President Trump failed to implement a national testing strategy early on, which is important to identify who is infected and prevent the spread of the disease. On the other hand, Prime Minister Justin Trudeau always acknowledged the severity of the public health crisis and he encouraged citizens to follow public safety guidelines. Additionally, Trudeau made widespread testing a national priority. 3) Canada’s culture emphasizes social responsibility and collective action, which is important when trying to encourage citizens to comply with social distancing guidelines and mask mandates. On the other hand, American culture is much more individualistic, and many Americans have not complied with social distancing guidelines because they believe doing so is an infringement on their liberty. 4) Canada has a universal healthcare model which is both coordinated and equitable; whereas the United States has a more fragmented healthcare system which made implementing a cohesive response to the public health crisis more difficult. Because all Canadians have access to health insurance, citizens were able to seek medical treatment when they were experiencing symptoms of the coronavirus. On the other hand, 1 in 5 non-elderly Americans are uninsured, and so this demographic was more reluctant to seek medical consultation due to their inability to cover out-of-pocket costs and medical expenses. While the Canadian government responded better to the coronavirus than the American government in 2020, the narrative shifted during 2021. The United States has been much more successful than Canada with vaccine production and distribution. The key driver behind American success in 2021 is that the United States has the largest and most resourceful pharmaceutical industry in the world. These findings are important because they provide insight regarding what constitutes a strong government response to a pandemic. Pandemics have occurred throughout history and they will continue to emerge in the future. This paper offers key takeaways and lessons about how governments should handle the next public health crisis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".