Comparison of COVID-19 Vaccination Rollout Approaches across Canada: Case Studies of Four Diverse Provinces
Bibliographic record
Abstract
Across Canada, there were notable differences in the rollout of provincial/territorial COVID-19 vaccination programs, reflecting diverse sociodemographic profiles, geopolitical landscapes, health system designs, and pandemic experiences. We collected information regarding underlying principles and goals, governance and authority, transparency and diversity of communications, activities to strengthen infrastructure and workforce capacity, and entitlement and access in four diverse provinces (British Columbia, Saskatchewan, Ontario, Nova Scotia). Through cross-case analysis, we observed significant differences in provincial rollouts of the primary two-dose vaccination series in adults between December 2020 and December 2021. Nova Scotia was the only province to state explicit coverage goals and adhere to plans tying coverage to the relaxation of public health measures. Both Nova Scotia and British Columbia implemented fully centralized vaccination booking systems. In contrast, Saskatchewan's initial highly centralized approach enabled the rapid delivery of first doses; however, rollout of second doses was slower and more decentralized, occurring primarily through community pharmacies. In alignment with its decentralized health system, Ontario pursued a regionalized approach, primarily led by its existing public health unit network. Our research suggests explicit goals, centralized booking, and flexible delivery strategies improved uptake; however, ongoing learning will be crucial for informing the success of future vaccination efforts.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".