Importance of occupation for SARS-CoV-2 seroprevalence and COVID-19 vaccination among correctional workers in Quebec, Canada: A cross-sectional study
Bibliographic record
Abstract
Background: Correctional workers are at increased risk of SARS-CoV-2 infection. We examined the seroprevalence of SARS-CoV-2, determined the effects of carceral and occupational exposures on seropositivity, and explored predictors of COVID-19 vaccine uptake among correctional workers in Quebec, Canada. Methods: serology test) and self-reported COVID-19 vaccination status ("fully vaccinated" defined as two doses or prior infection plus one dose), respectively. Poisson regression models with robust standard error were used to examine the effect of occupational variables with SARS-CoV-2 seropositivity and predictors of COVID-19 vaccine uptake. Estimates are presented as crude and adjusted prevalence ratios (aPR) with 95% confidence intervals (95% CI). Results: From 14 July to 15 November 2021, 105/600 (18%) correctional workers tested positive across three prisons (range 11-21%); 76% were fully vaccinated. Seropositivity was affected by prison occupation (aPR 1.59, 95% CI 1.11-2.27 for correctional officers vs. all other occupations) and low perceived concern of SARS-CoV-2 acquisition (aPR 1.62, 95% CI 1.11-2.38 for not/hardly worried vs. somewhat/extremely worried). Predictors of being fully vaccinated included race/ethnicity (aPR 0.86, 95% CI 0.76-0.99 for visible minority vs. White), presence of comorbidities (aPR 1.14, 95% CI 1.02-1.28 for > 2 vs. none), and prison occupation (aPR 0.82, 95% CI 0.73-0.92 for correctional officers vs. all other occupations). Conclusions: Correctional officers were most likely to have acquired SARS-CoV-2, but least likely to be vaccinated, underscoring the importance of addressing both occupational risks and COVID-19 vaccine hesitancy to mitigate future outbreaks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".