SARS-CoV-2 testing, infection and outcomes among Ontario physicians: a descriptive population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Health care workers have a higher risk of acquiring SARS-CoV-2 infection than the general population. Our study reports on SARS-CoV-2 testing, infection and associated outcomes in Ontario physicians before SARS-CoV-2 vaccination became available on Dec. 14, 2020. METHODS: We conducted a descriptive, population-based cohort study of physicians in Ontario, Canada, from Jan. 25 to Dec. 31, 2020. We included physicians and postgraduate medical trainees who were residents of Ontario and registrants with the College of Physicians and Surgeons of Ontario during the study period. We examined the proportion of physicians tested for SARS-CoV-2 infection, the proportion who tested positive, and how testing and infections varied by certain physician characteristics. We reported on clinical outcomes associated with infection, including hospital admission and death. RESULTS: Of 41 208 physicians (mean age 47 yr; 56.1% male), 19 116 (46.4%) were tested at least once for SARS-CoV-2 infection; 358 tested positive (0.9%). No physicians died within 30 days of testing positive; however, 20/358 (5.6%) were admitted to hospital. By specialty, the proportion tested was highest among postgraduate medical trainees (2531/4125 [61.4%]), emergency physicians (281/478 [58.8%]), infectious disease physicians (33/67 [49.3%]) and family physicians (8857/18 553 [47.7%]). The proportion who tested positive was highest among internal medicine physicians (44/3499 [1.3%]), postgraduate medical trainees (47/4125 [1.1%]) and family physicians (171/18 553 [0.9%]). Of 2290 physicians who worked in long-term care, 1636 (71.4%) were tested and 25 (1.1%) tested positive. INTERPRETATION: During the prevaccination period of the COVID-19 pandemic in Ontario, nearly half of all physicians in the province were tested at least once for SARS-CoV-2 infection, 0.9% tested positive and none died. These findings may reflect the public health measures that were implemented in the province during this period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".