Bibliographic record
Abstract
<div> Understanding patterns of SARS-CoV-2 seroprevalence among Long-Term Care Home and Retirement Home (LTCH/RH) staff is critical to designing effective public health interventions. We estimated SARS-CoV-2 seroprevalence among LTCH/RH staff in Ontario, Canada between May 2021-October 2022 using a cross-sectional analysis. Eligible participants completed a demographic questionnaire and provided a dried blood spot sample. Positive seroprevalence was defined as the proportion of individuals in a population who were positive for a SARS-CoV-2 infection, determined using anti-nucleocapsid total IgG antibodies analyzed with a validated chemiluminescent ELISA. We report age-adjusted prevalence ratios [PR; confidence interval, CI] by participant socio-demographic, household, neighbourhood, and occupational characteristics and stratified the analyses over two time periods (period 1: 2021-05-17 to 2021-12-31; period 2: 2022-01-02 to 2022-10-25). A total of 603 staff were included in our analysis; n=235 (39%) were enrolled in period 1 and n=368 (61%) were enrolled in period 2. Seroprevalence was 24% and 44% in periods 1 and 2, respectively. Age-adjusted prevalence ratios were nearly 2-fold higher among Black [PR 1.78; CI 1.28-2.48], East and Southeast Asian [PR 1.55, CI 1.18-2.04] and other racialized participants [PR 1.42, CI 1.03-1.96] compared to White participants. We did not observe a pattern across household characteristics, although we observed a trend towards higher seropositivity among participants living in COVID-19 hotspots. Prevalence ratios were lower for participants in higher income neighbourhoods [PR 0.72, CI 0.58-0.98]. We did not observe variability in seroprevalence across occupational characteristics with the exception of paid sick leave which was higher among participants with home-provided paid sick leave at the time of the survey [PR 0.58, CI 0.45-0.75]. Among LTCH/RH staff, we found important sources of variability of SARS-CoV-2 seroprevalence and strong correlations with socioeconomic disparities. Our findings show the importance of designing equity-rooted health interventions that recognize the intersection between community and the workplace. </div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.713 | 0.006 |
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; both teacher heads agree on what is shown here.
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".