COVID-19 infection in the Canadian household population
Bibliographic record
Abstract
Background: Certain population groups face a disproportionate burden of exposure to COVID-19. This study examined characteristics of Canadians living in private households in fall 2020 and winter 2021 who had been infected with COVID-19. Data and Methods: With an online questionnaire and an at-home finger-prick blood test, the Canadian COVID-19 Antibody and Health Survey was designed to estimate the seroprevalence of COVID-19 infection among people in private households in Canada. Data were collected from respondents aged 1 or older in the 10 provinces and the three territorial capitals, from November 2020 to April 2021. Descriptive statistics and logistic regression were used to identify characteristics that were associated with being seropositive for a past COVID-19 infection. Gender differences in observed associations were examined. Results: After covariate adjustment, younger age and visible minority status were associated with an increased likelihood of being seropositive for a past COVID-19 infection. For males, having a visible minority status, having less education and living in a multi-unit dwelling increased the likelihood of being seropositive. Females were more likely to have been seropositive if they worked in health care in direct contact with others. Interpretation: As Canada navigates the fifth and possibly a sixth wave of the pandemic, understanding who was more likely to be infected in earlier waves can help ongoing public health efforts to stop the transmission of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".