Structural and Systematic Discrimination Driven Misinformation
Notice bibliographique
Résumé
Introduction: While the world is focused on mitigating the impacts of COVID-19, the overwhelming need to focus on health literacy and communication is overlooked. As a pandemic to occur in a world of globalized communication, the spread of misinformation has presented crucial challenges in not only mitigating the transmission at the clinical level but has also impacted the way people have approached and experienced it. Misinformation during the pandemic has been heavily associated with the experiences of marginalized populations, and thus, can say, is driven by structural and systematic discrimination, which perpetuates mistrust and influences the perception. Through the Social Determinants of Health (SDOH) framework, this review aims to critically analyze the Public Health responses considering the social, cultural, and economic conditions that impact the inequity-driven experiences. Public health responses to the pandemic, especially during the first wave in Ontario, were heavily focused on social distancing, staying at home, and hygiene practices to lower the transmission of the virus. However, the interaction with these regulations varies depending on the different SDOH impacting the population and can directly cause the evolving mistrust in the messaging, as it may not be coherent with the experiences. The SDOH such as housing, income inequality, and language barriers, neighbourhood density, and cultural beliefs all play a role in the effectiveness of health literacy and communication and are already widely impacted by structural and systematic discrimination. Methods: A literature review was conducted to collect relevant data using the themes of Social Determinants of Health and misinformation during COVID-19 among marginalized populations. Of the articles, 25 articles were selected for when they matched the theme. Data was collected by a rigorous review of the selected articles. Results: The results of the search highlighted the impacts of misinformation during COVID-19 among individuals who were of lower socioeconomic status (SES), had diverse cultural backgrounds and were impacted by various social determinants. Findings suggested that communities who faced chronic systemic and structural barriers with inequitable social determinants, had higher exposure to misinformation. Discussion: The results of the literature review highlighted the need for an inclusive and upstream approach for public health responses. Much of the fear and disconnect caused by the misinformation of the pandemic is driven by the pre-existing structural and systematic discrimination. To better understand and address the harmful impacts, a more community-based approach is needed to tackle the stigma associated with the messaging of public health strategies. Individuals of marginalized populations need to feel more included to build a relationship where information provided will be perceived without mistrust and can lead to more accurate information consumption. If populations such as those of lower SES, feel that social distancing and essential travelling is the only way to prevent the risk of infection, then they may not have much trust in the system's response and may depend on misinformation provided by places of more familiarity as they are facing conditions that don’t allow them to follow the regulations. Health literacy/communication remains an impactful method in mitigating the concerns of misinformation and should be inclusive of the various intersections of the Social Determinants of Health at the community level. Only by including various cultural, social, and economic experiences can public health messaging reach populations.
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 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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».