COVID-19 vaccine uptake among Ontario physicians: a descriptive population-based retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To determine COVID-19 vaccine uptake among physicians in Ontario, Canada from 14 December 2020 to 13 February 2022. DESIGN: Population-based retrospective cohort study. SETTING: All registered physicians in Ontario, Canada using data from linked provincial administrative healthcare databases. PARTICIPANTS: 41 267 physicians (including postgraduate trainees) who were Ontario residents and registered with the College of Physicians and Surgeons of Ontario were included. Physicians who were out of province, had not accessed Ontario Health Insurance Plan-insured services for their own care for ≥5 years and those with missing identifiers were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were the proportions of physicians who were recorded to have received at least one, at least two and three doses of a Health Canada-approved COVID-19 vaccine by study end date. Secondary outcomes were how uptake varied by physician characteristics (including age, sex, specialty and residential location) and time elapsed between doses. RESULTS: Of 41 267 physicians, (56% male, mean age 47 years), 39 359 (95.4%) received at least one dose, 39 148 (94.9%) received at least two doses and 35 834 (86.8%) received three doses of a COVID-19 vaccine. Of those who received three doses, the proportions were 90.4% among those aged ≥60 years and 81.2-89.5% among other age groups; 88.7% among family physicians and 89% among specialists. 1908 physicians (4.6%) had no record of vaccination, and this included 3.4% of family physicians and 4.1% of specialists; however, 28% of this group had missing specialty information. CONCLUSIONS: In Ontario, within 14 months of COVID-19 vaccine availability, 86.8% of physicians had three doses of a COVID-19 vaccine, compared with 45.6% of the general population. Findings may signify physicians' confidence in the safety and effectiveness of COVID-19 vaccines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".