Social Vulnerability and Compliance With World Health Organization Advice on Protective Behaviors Against COVID-19 in African and Asia Pacific Countries: Factor Analysis to Develop a Social Vulnerability Index
Notice bibliographique
Résumé
BACKGROUND: COVID-19 protective behaviors are key interventions advised by the World Health Organization (WHO) to prevent COVID-19 transmission. However, achieving compliance with this advice is often challenging, particularly among socially vulnerable groups. OBJECTIVE: We developed a social vulnerability index (SVI) to predict individuals' propensity to adhere to the WHO advice on protective behaviors against COVID-19 and identify changes in social vulnerability as Omicron evolved in African countries between January 2022 and August 2022 and Asia Pacific countries between August 2021 and June 2022. METHODS: In African countries, baseline data were collected from 14 countries (n=15,375) during the first Omicron wave, and follow-up data were collected from 7 countries (n=7179) after the wave. In Asia Pacific countries, baseline data were collected from 14 countries (n=12,866) before the first Omicron wave, and follow-up data were collected from 9 countries (n=8737) after the wave. Countries' socioeconomic and health profiles were retrieved from relevant databases. To construct the SVI for each of the 4 data sets, variables associated with COVID-19 protective behaviors were included in a factor analysis using polychoric correlation with varimax rotation. Influential factors were adjusted for cardinality, summed, and min-max normalized from 0 to 1 (most to least vulnerable). Scores for compliance with the WHO advice were calculated using individuals' self-reported protective behaviors against COVID-19. Multiple linear regression analyses were used to assess the associations between the SVI and scores for compliance to WHO advice to validate the index. RESULTS: In Africa, factors contributing to social vulnerability included literacy and media use, trust in health care workers and government, and country income and infrastructure. In Asia Pacific, social vulnerability was determined by literacy, country income and infrastructure, and population density. The index was associated with compliance with the WHO advice in both time points in African countries but only during the follow-up period in Asia Pacific countries. At baseline, the index values in African countries ranged from 0.00 to 0.31 in 13 countries, with 1 country having an index value of 1.00. The index values in Asia Pacific countries ranged from 0.00 to 0.23 in 12 countries, with 2 countries having index values of 0.79 and 1.00. During the follow-up phase, the index values decreased in 6 of 7 African countries and the 2 most vulnerable Asia Pacific countries. The index values of the least vulnerable countries remained unchanged in both regions. CONCLUSIONS: In both regions, significant inequalities in social vulnerability to compliance with WHO advice were observed at baseline, and the gaps became larger after the first Omicron wave. Understanding the dimensions that influence social vulnerability to protective behaviors against COVID-19 may underpin targeted interventions to enhance compliance with WHO recommendations and mitigate the impact of future pandemics among vulnerable groups.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».