SARS-CoV-2 antibodies in Ontario health care workers during and after the first wave of the pandemic: a cohort study
Bibliographic record
Abstract
Background: Health care workers have a critical role in the pandemic response to COVID-19 and may be at increased risk of infection. The objective of this study was to assess the seroprevalence of SARS-CoV-2 immunoglobulin G (IgG) antibodies among health care workers during and after the first wave of the pandemic. Methods: We conducted a prospective multicentre cohort study involving health care workers in Ontario, Canada, to detect IgG antibodies against SARS-CoV-2. Blood samples and self-reported questionnaires were obtained at enrolment, at 6 weeks and at 12 weeks. A community hospital, tertiary care pediatric hospital and a combined adult–pediatric academic health centre enrolled participants from Apr. 1 to Nov. 13, 2020. Predictors of seropositivity were evaluated using a multivariable logistic regression, adjusted for clustering by hospital site. Results: Among the 1062 health care workers participating, the median age was 40 years, and 834 (78.5%) were female. Overall, 57 (5.4%) were seropositive at any time point (2.5% when participants with prior infection confirmed by polymerase chain reaction testing were excluded). Seroprevalence was higher among those who had a known unprotected exposure to a patient with COVID-19 (p < 0.001) and those who had been contacted by public health because of a nonhospital exposure (p = 0.003). Providing direct care to patients with COVID-19 or working on a unit with a COVID-19 outbreak was not associated with higher seroprevalence. In multivariable logistic regression, presence of symptomatic contacts in the household was the strongest predictor of seropositivity (adjusted odds ratio 7.15, 95% confidence interval 5.42–9.41). Interpretation: Health care workers exposed to household risk factors were more likely to be seropositive than those not exposed, highlighting the need to emphasize the importance of public health measures both inside and outside of the hospital.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".