COVID-19 vaccines: a geographic, social and policy view of vaccination efforts in Ontario, Canada
Bibliographic record
Abstract
Abstract In recent months, more studies are emerging regarding how various nations and regions fared during the initial two years of the COVID-19 pandemic. Canada is cited as an example of a country that had performed reasonably well versus other countries with comparable infrastructures and health care systems (Razek et al., 2022). The reason is largely attributed to a combination of several public health measures coupled with widespread vaccination uptake, as a result of a country-wide vaccination campaign. This paper is based on a keynote talk given at the Autumn 2021 CJRES Annual Conference, by Dr. Isaac I. Bogoch. Dr Bogoch is an Associate Professor in the Department of Medicine at the University of Toronto, and an Infectious Diseases Consultant in the Division of Infectious Diseases at the Toronto General Hospital. Dr. Bogoch was a member of Ontario’s Vaccine Distribution Taskforce, which helped guide vaccine policy during the initial rollout of COVID-19 vaccines between December 2020 through August 2021. Dr. Bogoch explains the unique vaccine policy in the Province of Ontario and in particular the social innovation around prioritising the most vulnerable and disadvantaged neighbourhoods first, thus leading to an important intra-regional social policy view of vaccine efforts on the path beyond the ‘emergency phase’ of the COVID-19 pandemic. What is clearly obvious from his presentation is the heightened role of urban geography tools and techniques and intra-regional policy in vaccine equity efforts. Policy lessons learned in Ontario may help us sort out future urban, social, economic, epidemiologic and public health challenges and their sometimes-complex intersections in regions, economy and society. The following is an edited transcript from Dr. Bogoch’s talk.
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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 | 0.001 |
| 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 | 0.000 |
| 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".