Cutting the threads of patchwork policy : the impact of decentralization on pandemic containment in Nova Scotia and British Columbia
Bibliographic record
Abstract
This thesis explores the topic of Canadian federalism and decentralization in the context of the COVID-19 pandemic.Federalism has shaped Canadian healthcare and over time has led to discrepancies in health policy and administration in terms of the distribution of federal, provincial, and territorial powers, and the institutional design of healthcare that varies across jurisdictions.The emergence of the COVID-19 pandemic has thrust public health to the forefront of policy at all levels, placing tension on Canada's already fragmented healthcare system.These tensions are analyzed further through a comparative case study of the provinces of Nova Scotia and British Columbia to demonstrate how the historic federal, provincial, territorial divide has impacted provincial containment of COVID-19 during the first wave of the pandemic (January 2020 -September 2020).A brief history of Canadian federalism is given in section one, followed by an assessment of the strict public health measures that are necessary to effectively contain the virus in section two, and lastly section three contains a case study of the provinces of Nova Scotia and British Columbia to analyze how these provinces were able to effectively manage the spread of the virus in the first wave.As their shared success began to diminish in the second wave of the pandemic, this thesis argues that a bottom-up, pan-Canadian health strategy could foster continued collaboration between the federal, provincial, and territorial governments, through the establishment of documented best practices to encourage the implementation of the public health measures needed to contain the virus.For it is in times like these; when entire healthcare systems across the country are called to action, that our "patchwork" model of healthcare governance manifests its true weakness and highlights the need for change.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".