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Enregistrement W4410892227 · doi:10.1101/2025.05.28.25327793

A Community-Engaged Public Health Research and Outreach Program for Migrant and Racialized Workers in Meat Processing to Mitigate COVID-19 Inequities

2025· preprint· en· W4410892227 sur OpenAlexafffundabout
Gabriel E. Fabreau, Eric Norrie, Linda Holdbrook, Minnella Antonio, Mohammad Yasir Essar, Michael Youssef, Adanech Sahilie, Mussie Yemane, Edna Ramirez-Cerino, Nour Hassan, Rabina Grewal, Zahra Hussain, Deyana Altahsh, Olivia Magwood, Ammar Saad, Maria Santana, Aleem Bharwani, Ingrid Nielssen, Samuel T. Edwards, Denise L. Spitzer, Annalee Coakley, Kevin Pottie

Notice bibliographique

RevuemedRxiv · 2025
Typepreprint
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueCOVID-19 Pandemic Impacts
Établissements canadiensWestern UniversityBruyèreUniversity of OttawaAlberta Medical AssociationUniversity of AlbertaUniversity of Calgary
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésOutreachCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakPublic healthMigrant workersSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Health equityEconomic growthPandemicPolitical scienceSociologyBusinessPublic relationsMedicineEconomicsNursingVirologyOutbreak

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,078

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0120,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0110,002
Communication savante0,0020,001
Science ouverte0,0020,007
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,448
Tête enseignante GPT0,447
Écart entre enseignants0,000 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2025
Routes d'admission3
Résumé présentoui

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