Vaccine and COVID-19 Trajectories: Equal vaccine rates do not reduce inequality in COVID-19 rates
Bibliographic record
Abstract
Researchers and public health officials posit that vaccine equity holds the key to ending the pandemic. Yet, most prior work on vaccine equity focuses on vaccine hesitancy and seldom compares the vaccine trajectories of neighborhoods with varying COVID-19 levels. Notably scarce are also studies that examine the extent to which vaccination helps reduce inequalities in the prevalence of COVID-19. Using administrative data from the City of Toronto, we compare the vaccine trajectories of neighborhoods with low, moderate, and high COVID-19 rates. We also examine whether disparities in COVID-19 rates by a neighborhood’s COVID-19 rates as vaccinations have increased. By mid-June 2021, differences in vaccination rates by the neighborhoods’ COVID-19 levels are small. The vaccination rollout has only had a small impact on disparities in COVID-19 rates across neighborhoods. Equality in vaccination rates is by no means a silver bullet to reduce inequalities in COVID-19 infections across neighborhoods with varying socio-demographic characteristics.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".