COVID-19 Vaccination Strategy for Ontario Using Age and Neighbourhood-Based Prioritization
Bibliographic record
Abstract
SARS-CoV-2 infection has taken a disproportionate toll on Ontario older adults, and on residents of disadvantaged and racialized urban neighbourhoods throughout the province. Prioritizing and implementing vaccine distribution for Ontarians based on both age and neighbourhood of residence could ensure that those at the highest risk of SARS-CoV-2 infection, and hospitalization, ICU admission or death from COVID-19 will be among the first to receive vaccines. This vaccine strategy will maximize the prevention of deaths and long-term morbidity, and best maintain health care system capacity by reducing hospitalizations and ICU admissions due to COVID-19 as compared with a strategy that prioritizes vaccination based on age alone (Figure 1). The strategy would not interfere with the ongoing and future vaccination of any specific high-risk population, as it is intended to guide the mass distribution of vaccines to the general Ontario population.
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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 | 0.002 | 0.033 |
| 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 | 0.001 | 0.000 |
| 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 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".