COVID-19 Vaccine Uptake in Southeastern Ontario, Canada: Monitoring and Addressing Health Inequities
Bibliographic record
Abstract
CONTEXT: Implementation of a population-based COVID-19 vaccine strategy, with a tailored approach to reduce inequities in 2-dose coverage, by a mid-sized local public health agency in southeastern Ontario, Canada. PROGRAM: Coverage maps and crude and age-standardized coverage rates by material and social deprivation, urban/rural status, and sex were calculated biweekly and reviewed by local public health planners. In collaboration with community partners, the results guided targeted strategies to enhance uptake for marginalized populations. EVALUATION: The largest gaps in vaccine coverage were for those living in more materially deprived areas and rural residents-coverage was lower by 10.9% (95% confidence interval: -11.8 to -10.0) and 9.3% (95% confidence interval: -10.4 to -8.1) for these groups compared with living in less deprived areas and urban residents, respectively. The gaps for all health equity indicators decreased statistically significantly over time. Targeted strategies included expanding clinic operating hours and availability of walk-in appointments, mobile clinics targeted to marginalized populations, leveraging primary care partners to provide pop-up clinics in rural and materially and socially deprived areas, and collaborating with multiple partners to coordinate communication efforts, especially in rural areas. DISCUSSION: The scale and scope of monitoring and improving local vaccine uptake are unprecedented. Regular review of health equity indicators provided critical situational awareness for decision makers, allowing partners to align and tailor strategies locally and in collaboration with one another. Health care providers and pharmacies/pharmacists are key partners who require innovative support to increase uptake in marginalized groups. Continued engagement of other community partners such as schools, municipalities, and local service groups is also crucial. A "hyper local" approach is needed along with commitment from partners in all sectors and at all levels to reduce barriers to vaccination that lie further upstream for marginalized groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".