SARS-CoV-2 infection among healthcare workers: the role of occupational and household exposures during the first three pandemic waves in Quebec, Canada
Bibliographic record
Abstract
Objective: We described the evolution of SARS-CoV-2 source of infection in a cohort of healthcare workers (HCWs) of Quebec, Canada, during the first three pandemic waves. We also estimated their household secondary attack rate (SAR) and its risk factors. Design: Cross-sectional surveys. Participants: HCWs with a SARS-CoV-2 infection confirmed by polymerasa chain reaction and diagnosed between March 2020 and May 2021. Methods: We collected demographic, clinical, vaccination, and employment information, self-reported perceived source of infection, and transmission to household members during the first three pandemic waves. SAR was calculated for households with ≥2 members where the HCW was the index case. A Poisson regression model estimated the association between risk factors and SAR. Results: Among the 11,670 HCWs completing the survey, 91%, perceived their workplace as the source of infection during the first wave (March-July 2020), 71% during the second wave (July 2020-March 2021), and 40% during the third wave (March-May 2021). Conversely, HCWs reported an increasing proportion of household-acquired infections with each wave from 4% to 14% and 33%, respectively. The overall household SAR of 7,990 HCWs living with ≥1 person was 30% (95%CI: 29-30). SAR increased with the presence of symptoms, older age, and during Alpha-variant predominant period. Conclusions: HCWs and their household members were largely affected during the first pandemic waves of COVID-19, but the relative importance of occupational exposure changed overtime. Pandemic preparedness in healthcare settings is essential to protect HCWs from emerging biological hazard exposures.
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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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".