Mass testing for asymptomatic COVID-19 infection among health care workers at a large Canadian hospital
Bibliographic record
Abstract
Background: The perceived risk of coronavirus disease 2019 (COVID-19) infection for health care workers (HCWs) is high. Although testing has focused on symptomatic HCWs, asymptomatic testing is considered by some to be an important strategy to limit occupational spread. Evidence on the results of large asymptomatic testing strategies in health care is, however, limited. This study examines the uptake and positivity of COVID-19 testing in a voluntary asymptomatic testing campaign at a large Canadian hospital. Methods: In addition to testing HCWs with symptoms, all asymptomatic staff were offered a COVID-19 test at Trillium Health Partners, a large Ontario hospital, from May 27 to June 15, 2020. Testing was offered in four waves, corresponding to the likelihood of exposure to COVID-19-positive patients. The mass asymptomatic testing campaign was offered when the hospital's community test positivity rate had declined to 5%. Results: Since March 16, the hospital has tested 51.3% of its 10,143-person workforce at least once. In the asymptomatic testing campaign for HCWs between May 27 and June 15, 27% of clinical and non-clinical staff received testing. No large differences were found in the proportions of clinical HCWs tested by their exposure to COVID-19-positive patients. In this campaign, 0.2% of asymptomatic HCWs tested positive. However, these individuals either had mild symptoms at testing and did not self-identify or became symptomatic after testing. Conclusions: At this large hospital with declining community prevalence, a mass asymptomatic testing campaign of HCWs found they had a very low likelihood of testing positive for COVID-19.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".