Examining the Relationship Between Workplace Industry and COVID-19 Infection
Bibliographic record
Abstract
OBJECTIVES: To control virus spread while keeping the economy open, this study aimed to identify individuals at increased risk of COVID-19 transmission in the workplace using rapid antigen screening data. METHODS: Among adult participants in a large Canadian rapid antigen screening program (January 2021-March 2022), we examined screening, personal, and workplace characteristics and conducted logistic regressions, adjusted for COVID-19 wave, screening frequency and location, role, age group, and geography. RESULTS: Among 145,814 participants across 2707 worksites, 6209 screened positive at least once. Workers in natural resources (odds ratio [OR] = 2.1 [1.73-2.55]), utilities (OR = 1.67 [1.38-2.03]), construction (OR = 1.35 [1.06-1.71]), and transportation/warehousing (OR = 1.32 [1.12-1.56]) had increased odds of screening positive; workers in education/health (OR = 0.62 [0.52-0.73]), leisure/hospitality (OR = 0.71 [0.56-0.90]), and finance (OR = 0.84 [0.71-0.99]) had lesser odds of screening positive, compared with professional/business services. CONCLUSIONS: Certain industries involving in-person work in close quarters are associated with elevated COVID-19 transmission. Continued reliance on rapid screening in these sectors is warranted.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".