A media surveillance analysis of COVID-19 workplace outbreaks in Canada and the United States
Bibliographic record
Abstract
A media surveillance analysis was conducted to identify COVID-19 workplace outbreaks and associated transmission risk for new and emerging occupations. We identified 1,111 unique COVID-19 workplace outbreaks using the Factiva database. Occupations identified in the media articles were coded to the 2016 National Occupational Classification (V1.3), and were compared and contrasted with the same occupation in the Vancouver School of Economics (VSE) COVID Risk/Reward Assessment Tool by risk rating. After nurse aides, orderlies and patient service associates (n=109, very high risk), industrial butchers and meat cutters, and poultry preparers and related workers had the most workplace outbreaks reported in the media (n=79) but were rated as medium risk for COVID-19 transmission in the VSE COVID Risk Tool. Outbreaks were also reported among material handlers (n=61) and general farm workers (n=28) but these occupations were rated medium-low risk and low risk, respectively. Food and beverage services (n=72) and cashiers (n=60) were identified as high risk occupations in the VSE COVID Risk Tool. Differences between the media results and the risk tool point to key determinants of health that compound the risk of COVID-19 exposure in the workplace for some occupations, and highlight the importance of collecting occupation data during a pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".