Seroprevalence of anti-SARS-CoV-2 antibodies among school and daycare children and personnel: Protocol for a cohort study in Montreal, Canada
Bibliographic record
Abstract
ABSTRACT Background Further evidence is needed to understand the contribution of schools and daycares to the spread of COVID-19 in the context of diverse transmission dynamics and continually evolving public health interventions. The Enfants et COVID-19: Étude de séroprévalence (EnCORE) study will estimate the seroprevalence and seroconversion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among school and daycare children and personnel. In addition, the study will examine associations between seroprevalence and socio-demographic characteristics and reported COVID-19 symptoms and tests, and investigates changes in health, lifestyle and well-being outcomes. Methods This study includes children and personnel from 62 schools and daycares in four neighbourhoods in Montreal, Canada. All children age 2-17 years attending one of the participating schools or daycares and their parents are invited to participate, as well as a sample of personnel members. Participants respond to brief questionnaires and provide blood samples, collected via dried blood spot (DBS), at baseline (October 2020-March 2021) and follow-up (May-June 2021). Questionnaires include socio-demographic and household characteristics, reported COVID-19 symptoms and tests, potential COVID-19 risk factors and prevention efforts, and health and lifestyle information. Logistic regression using generalized estimating equations will be used to estimate seroprevalence and seroconversion, accounting for school-level clustering. Discussion The results of the EnCORE study will contribute to our knowledge about SARS-CoV-2 transmission in schools and daycares, which is critical for decisions regarding school attendance and the management of school outbreaks through the remainder of this school year and beyond.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".