Sharpening our public health lens: advancing im/migrant health equity during COVID-19 and beyond
Notice bibliographique
Résumé
BACKGROUND: Differential impacts of the COVID-19 pandemic have brought deeply rooted inequities to the forefront, where increasing evidence has shown that racialized immigrant and migrant (im/migrant) populations face a disproportionate burden of COVID-19. Im/migrant communities may be worst affected by lockdowns and restrictive measures, face less opportunity to physically distance or stay home sick within 'essential' jobs, and experience severe barriers to healthcare. Insufficient attention to experiences of racialized im/migrants in current pandemic responses globally highlights an urgent need to more fulsomely address unmet health needs through an anti-racist, equity-oriented lens. This commentary aims to highlight the need for public health and clinical training, research, and policy to thoughtfully prioritize im/migrant health equity during and beyond the COVID-19 pandemic. MAIN TEXT: Global pandemic responses have neglected im/migrants by continuing to ignore or insufficiently address inequities, exacerbating COVID transmission, xenophobia, and occupational injustice. Deaths, illness, stress, and other negative outcomes of the overlapping epidemics of COVID-19 and structural racism disproportionately borne by racialized im/migrants suggest the urgent need for action. As evidence mounts about how im/migrants have been left behind in times of crises, we need enhanced focus on health equity within COVID-19 research and interventions, including research that examines and pursues structural interventions necessary to mitigate these impacts, and that identifies patterns and harms of xenophobic policy, structural racism, and white supremacy in shaping im/migrant health outcomes. We must also strengthen anti-racist and equity-oriented curriculum within health education, and ensure sufficient attention to the needs of im/migrant communities within public health, clinical, and research training. CONCLUSION: The COVID-19 pandemic has exacerbated and rendered more visible the deeply rooted health and social inequities faced by racialized im/migrants across diverse settings. We argue for a greater emphasis on equity-focused and anti-racist im/migrant health research, interventions, and training. Policymakers and practitioners must ensure that healthcare policies and practices do not exacerbate inequities, and instead meaningfully address unmet needs of communities, including racialized im/migrants. Ethical and respectful community engagement, commitment and collaboration with global, national, and local communities, policymakers, academics, and educators, as well as accountability across sectors, is critical.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».