Profile, Infection, and Vaccination Uptake: A Cohort of Canadian Retail Workers During the SARS-CoV-2 Pandemic
Bibliographic record
Abstract
Background/Objectives: Retail workers may have been at an increased risk of contracting SARS-CoV-2 during the COVID-19 pandemic. To better understand this group, we set up a longitudinal cohort to document the occurrence of SARS-CoV-2 infection, vaccination uptake and to study immune response. Methods: Participants were enrolled between 20 April and 22 October 2021 and attended up to 5 visits over 48 weeks. Information collected was: participant characteristics, SARS-CoV-2 detection tests performed, COVID-19 symptoms, and vaccination (influenza and SARS-CoV-2). Findings: We included 304 participants aged 18 to 75; of those, 117 had a first positive SARS-CoV-2 test, mostly (85.5%) during Omicron wave. Forty-two (13.8%) participants got seasonal influenza vaccine within the year (2020–2021) prior to the first visit, and 95.9% had received the primary series of 2 doses of SARS-CoV-2 vaccine by the beginning of Omicron wave. Participants vaccinated for influenza (adjusted hazard ratio (aHR) 2.48; 95% confidence interval (CI): 1.54–3.98) and older patients (aHR 2.39; 95% CI: 1.40–4.10), were more likely to get a first booster of SARS-CoV-2 vaccine compared to those who did not receive influenza vaccine. In contrast, participants who traveled (aHR 0,62; 95% CI: 0.43–0.91) or participated in frequent gatherings (aHR 0.58; 95% CI: 0.39–0.85) were less likely to be boosted. Conclusions: Variations in vaccine uptake that are usually observed within populations had little effect on completion of the primary SARS-CoV-2 vaccine series. However, these differences became apparent for booster doses, at a period during which most infections in this cohort were recorded.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".