SARS-CoV-2 infection among physicians over time in Ontario, Canada: a population-based retrospective cohort study
Bibliographic record
Abstract
AIM: To assess this risk of SARS-CoV-2 infection among Ontario physicians by specialty and in comparison with non-physician controls during the COVID-19 pandemic. METHODS: In this retrospective cohort study, the primary outcome was incident SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR). Secondary outcomes were hospitalization, use of critical care, and mortality. RESULTS: From March 1, 2020 to December 31, 2022, 6172/30 617 (20%) active Ontario physicians tested positive for SARS-CoV-2. Infection was less likely if physicians were older (OR 0.78 [0.76-0.81] per 10 years), rural residents (OR 0.70 [0.59-0.83]), and lived in more marginalized neighborhoods (OR 0.74 [0.62-0.89]), but more likely if they were female (OR 1.14 [1.07-1.22]), worked in long-term care settings (OR 1.16 [1.02-1.32]), had higher patient volumes (OR 2.05 [1.82-2.30] for highest vs lowest), and were pediatricians (OR 1.25 [1.09-1.44]). Compared with community-matched controls (n=29 763), physicians had a higher risk of infection during the first two waves of the pandemic (OR 1.38 [1.20-1.59]) but by wave 3 the risk was no longer significantly different (OR 0.93 [0.83-1.05]). Physicians were less likely to be hospitalized within 14 days of their first positive PCR test than non-physicians (P<0.0001), but there was no difference in the use of critical care (P=0.48) or mortality (P=0.15). CONCLUSION: Physicians had higher rates of infection than community-matched controls during the first two waves of the pandemic in Ontario, but not from wave 3 onward. Physicians practicing in long-term care facilities and pediatricians were more likely to test positive for SARS-CoV-2 than other physicians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 | 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".