Muslim communities and Covid-19: A report on the impacts of the Coronavirus pandemic on Muslims in Birmingham
Notice bibliographique
Résumé
The Covid-19 pandemic impacted on Muslim communities in Birmingham in a number of distinctive ways for Muslims as a faith group. National level data on ethnicity and Covid-19 indicates that groups which are predominantly Muslim, namely Bangladeshi and Pakistani groups, were among those most severely impacted in the first wave of the pandemic. As the second and third waves of the pandemic played out, mortality risks from Covid-19 remained highest for individuals of Bangladeshi backgrounds, with Pakistani groups also remaining at an elevated level of risk. Findings from our stage 1 questionnaire carried out with 72 Muslims living in Birmingham as a scoping exercise indicated that disparate health impacts experienced by participants were broadly consistent with the above. Around 21% of participants reported working in an environment with an elevated risk of exposure to Covid 19, and this is higher than the national average for the proportion of key workers from minority ethnic groups, who made up 14% of key workers in the first quarter of the pandemic (ONS 2020). 51% of participants reported experiencing stress or anxiety, with this appearing to impact most for those aged 25-34, with 68% reporting stress or anxiety directly resulting from the pandemic. 32% of all participants reported a change in prayer habits with 51% of men reporting a change compared with just 10% of women. The proportion of participants who were unvaccinated was 18%, which is 5.5 percentage points higher than the national average for Bangladeshi groups of 12.5%, and 2 percentage points higher than the national average of 16% for Pakistani groups (ONS 2022c). When broken down across ethnic group, 25% of participants who identified as Bangladeshi were unvaccinated compared with 13% of Pakistani participants. This shows that whilst the proportion of participants who identified as Pakistani that were unvaccinated is lower than the national average by two percentage points, the proportion of Bangladeshi participants who were unvaccinated was double the proportion of Bangladeshis who were unvaccinated in the national population.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».