Trends in Testing for SARS-CoV-2 Among Healthcare Workers in a Canadian Cohort Study During the COVID-19 Pandemic, June 2020 to November 2023
Bibliographic record
Abstract
Background: While testing healthcare workers (HCWs) for SARS-CoV-2 is important to reduce transmission within healthcare settings, understanding the self-reported patterns of testing is important for interpreting vaccine effectiveness and other COVID-19-related information. Objective: Using longitudinal data from the COVID-19 Cohort Study, this study described trends in SARS-CoV-2 testing among Canadian HCWs between June 2020 and November 2023. Methods: HCWs completed an illness report for each instance of SARS-CoV-2 testing and episodes of symptoms compatible with COVID-19 even if untested. Overall rates of testing were calculated along with rates stratified by participating province, reason for testing, and COVID-19 vaccination status. Results: Rates of testing for SARS-CoV-2 generally mirrored rates of hospitalization for COVID-19 among Canadians. Rates of testing were highest during the Omicron BA.1 wave and varied by region, while vaccination status did not impact rates. The most commonly reported reason for testing was for symptoms; testing for known/possible exposure or routine reasons greatly decreased after the Omicron BA.1 wave. In participants who were tested for episodes of symptomatic illness, the mean time to first test was 1.3 days. Reported retesting after an initial negative result remained low throughout the study period. Conclusions: Understanding testing behaviours is important for public health decision-making including the analysis and interpretation of case data and vaccine effectiveness studies. It can also highlight possible missed case-finding opportunities in healthcare settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".