The mental health experiences of ethnic minorities in the UK during the Coronavirus pandemic: A qualitative exploration
Notice bibliographique
Résumé
Background Worldwide, the Coronavirus pandemic has had a major impact on people's health, lives, and livelihoods. However, this impact has not been felt equally across various population groups. People from ethnic minority backgrounds in the UK have been more adversely affected by the pandemic, especially in terms of their physical health. Their mental health, on the other hand, has received less attention. This study aimed to explore the mental health experiences of UK adults from ethnic minorities during the Coronavirus pandemic. This work forms part of our wider long-term UK population study “Mental Health in the Pandemic.” Methods We conducted an exploratory qualitative study with people from ethnic minority communities across the UK. A series of in-depth interviews were conducted with 15 women, 14 men and 1 non-binary person from ethnic minority backgrounds, aged between 18 and 65 years old (mean age = 40). We utilized purposefully selected maximum variation sampling in order to capture as wide a variety of views, perceptions and experiences as possible. Inclusion criteria: adults (18+) from ethnic minorities across the UK; able to provide full consent to participate; able to participate in a video- or phone-call interview. All interviews took placeviaMS Teams or Zoom. The gathered data were transcribed verbatim and underwent thematic analysis following Braun and Clarke carried out using NVivo 12 software. Results The qualitative data analysis yielded seven overarching themes: (1) pandemic-specific mental health and wellbeing experiences; (2) issues relating to the media; (3) coping mechanisms; (4) worries around and attitudes toward vaccination; (5) suggestions for support in moving forward; (6) best and worst experiences during pandemic and lockdowns; (7) biggest areas of change in personal life. Generally, participants' mental health experiences varied with some not being affected by the pandemic in a way related to their ethnicity, some sharing positive experiences and coping strategies (exercising more, spending more time with family, community cohesion), and some expressing negative experiences (eating or drinking more, feeling more isolated, or even racism and abuse, especially toward Asian communities). Concerns were raised around trust issues in relation to the media, the inadequate representation of ethnic minorities, and the spread of fake news especially on social media. Attitudes toward vaccinations varied too, with some people more willing to have the vaccine than others. Conclusion This study's findings highlight the diversity in the pandemic mental health experiences of ethnic minorities in the UK and has implications for policy, practice and further research. To enable moving forward beyond the pandemic, our study surfaced the need for culturally appropriate mental health support, financial support (as a key mental health determinant), accurate media representation, and clear communication messaging from the Governments of the UK.
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 | 0,009 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,012 | 0,012 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».