Characteristics of primary care practices by proportion of patients unvaccinated against SARS-CoV-2: a cross-sectional cohort study
Bibliographic record
Abstract
BACKGROUND: Variations in primary care practices may explain some differences in health outcomes during the COVID-19 pandemic. We sought to evaluate the characteristics of primary care practices by the proportion of patients unvaccinated against SARS-CoV-2. METHODS: We conducted a population-based, cross-sectional cohort study using linked administrative data sets in Ontario, Canada. We calculated the percentage of patients unvaccinated against SARS-CoV-2 enrolled with each comprehensive-care family physician, ranked physicians according to the proportion of patients unvaccinated, and identified physicians in the top 10% (v. the other 90%). We compared characteristics of family physicians and their patients in these 2 groups using standardized differences. RESULTS: = 906) were more likely to be male, to have trained outside of Canada, to be older, and to work in an enhanced fee-for-service model than those in the remaining 90%. Vaccine coverage (≥ 2 doses of SARS-CoV-2 vaccine) was 74% among patients of physicians with the largest proportion of unvaccinated patients, compared with 87% in the remaining patient population. Patients in the top 10% group tended to be younger and live in areas with higher levels of ethnic diversity and immigration and lower incomes. INTERPRETATION: Primary care practices with the largest proportion of patients unvaccinated against SARS-CoV-2 served marginalized communities and were less likely to use team-based care models. These findings can guide resource planning and help tailor interventions to integrate public health priorities within primary care practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".