Vaccines alone will not prevent COVID-19 outbreaks among migrant workers—the example of meat processing plants
Bibliographic record
Abstract
BACKGROUND: Migrant populations in high-income countries have faced myriad health and social inequities during the COVID-19 pandemic. Migrants often work in frontline essential services that expose them to COVID-19. Migrant workers in meat processing plants have endured large COVID-19 outbreaks across multiple countries. OBJECTIVES: We examine current scientific evidence around COVID-19 transmission, outcomes, and prevention for migrant workers and highlight meat processing plants as an example. SOURCES: We performed a series of PubMed searches between January 1, 2020 and January 12, 2022. CONTENT: Migrant workers in high-income countries often work in occupations at high risk for COVID-19 transmission, contract COVID-19 at higher rates, and experience worse outcomes than native-born counterparts. For example, meat processing plants represent almost ideal environments for rapid and large-scale SARS-CoV-2 viral transmission; often, large migrant workforces confined to small workspaces perform physically demanding work in noisy environments that require shouting to communicate, increasing workers' respiratory rates and the quantity of aerosolized droplets expelled and thus increasing viral transmission risk. Although enhanced vaccination outreach programs remain an important equity approach for migrant worker safety, they alone are insufficient. The emergence and rapid spread of multiple increasingly transmissible SARS-CoV-2 variants of concern with variable vaccine escape properties, including Omicron in November 2021, highlight the importance of improved infection prevention and control strategies to protect migrant workers. Across countries, strategies such as improving ventilation and mask quality in many high-risk occupational settings are already required by employment law. Universal mandatory vaccination program should also be considered. IMPLICATIONS: COVID-19 transmission prevention for migrant workers requires an aggressive multicomponent plan that includes (a) improved on-site ventilation and infection prevention and control strategies; (b) improved social supports such as paid sick leave; (c) mobile vaccination clinics and community engagement to overcome vaccine hesitancy and barriers; and (d) consideration of universal mandatory vaccination programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".