A Community-Engaged Public Health Research and Outreach Program for Migrant and Racialized Workers in Meat Processing to Mitigate COVID-19 Inequities
Bibliographic record
Abstract
Abstract Objective COVID-19 has disproportionately impacted migrant workers in meat processing industries causing mass outbreaks and fatalities. Implementing community based participatory research (CBPR) methods may increase public health engagement, but developing the prerequisite trust required is hindered during a public health crisis. Methods We used CBPR methods to recruit, train and integrate six community scholars representing various racialized ethnocultural minorities into public health research and outreach operations. We present an organizational case study of their experiences across multiple Canadian meat plants affected by mass COVID-19 outbreaks. We used administrative documents to describe the project setting, training, and roles across research and vaccine operations between March 2020 and December 2022. Scholars then completed reflexivity activities using narrative analysis to summarize their experiences and impacts on themselves, migrant workers, and their communities. Finally, we integrated our data through scholars’ reflections to investigate how their narrative analysis was reflected in the administrative, quantitative and time series data. Findings We summarize three study phases; 1) Scholars’ recruitment and training; 2) early community engagement; 3) community outreach vaccinations. After Scholars’ team integration, initial worker study recruitment attempts failed due to mistrust and fear of employer reprisals. Scholars built trust among workers playing key roles in nine onsite meat plant occupational and community outreach COVID-19 vaccine clinics. successfully surveyed 191, and interviewed 43 workers in seven primary languages across eleven meat plants between January 2021 and February 2022. Scholars described their roles, successful outreach strategies, learnings, prerequisite skills, and intimate interactions that contributed motivation and meaning. Key insights included empathetically validating workers’ experiences, translating stories into advocacy, and the importance of community presence combining public health research and outreach. Conclusion During public health crises, community-academic-healthcare partnerships can rapidly implement multicultural CBPR strategies to effectively engage migrant workers concurrently in both research and public health outreach. Funding Canadian Institutes of Health Research (CIHR Application no. 469206) Key Messages 1. What is already known on this topic? The COVID-19 pandemic disproportionately impacted migrant workers in meat processing industries, leading to significant outbreaks, poor health outcomes, and fatalities across multiple high-income countries. Existing literature highlights the challenges of engaging these workers in public health research and outreach operations due to structural barriers, precarious economic and immigration statuses, and mistrust towards health authorities. Community-based participatory research (CBPR) methods can overcome these barriers; however, they fundamentally depend on developing trust between academic and healthcare partners and migrant workers, which is very difficult during public health crises such as mass COVID-19 outbreaks in meat processing facilities. 2. What this study adds? This study introduces and evaluates a novel, rapidly developed community-based participatory research (CBPR) program that integrated six community leaders called ‘Community Scholars,’ representing various racialized ethnocultural minorities, into public health research and outreach operations concurrently. It details how community scholars were trained and integrated into teams to engage migrant workers in research and vaccine outreach operations. The study outlines the program’s failures, successes, reflections, and key learnings, to overcome traditional participation barriers. 3. How this study might affect research, practice or policy? These findings suggest that employing CBPR methods rapidly with active community involvement can synergistically enhance engagement and trust among migrant workers across both public health research and operations. This study provides insights that may serve as a blueprint for similar contexts, informing future public health strategies and policies to better manage crises involving socially vulnerable migrant populations. It emphasizes the potential of community-driven approaches to bridge gaps in public health research, practice and policy, particularly during emergencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".