Experiences and Views of Older Adults of South Asian, Black African, and Caribbean Backgrounds About the Digitalization of Primary Care Services Since the COVID-19 Pandemic: Qualitative Focus Group Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic from 2020 to 2022 prompted governments worldwide to enforce lockdowns and social restrictions, alongside the rapid adoption of digital health and care services. However, there are concerns about the potential exclusion of older adults, who face barriers to digital inclusion, such as age, socioeconomic status, literacy level, and ethnicity. OBJECTIVE: This study aims to explore the experiences of older adults from the 3 largest minoritized ethnic groups in England and Wales-people of South Asian, Black African, and Caribbean backgrounds-in the use of digitalized primary care services since the beginning of the COVID-19 pandemic. METHODS: In total, 27 individuals participated in 4 focus groups (April and May 2023) either in person or via online videoconferencing. Patient and public involvement and engagement were sought through collaboration with community organizations for focus group recruitment and feedback on the topic guide. Data were analyzed using framework analysis. RESULTS: This paper summarizes the perspectives of 27 older adults from these 3 minoritized ethnic groups and identifies four key themes: (1) service accessibility through digital health (participants faced difficulties accessing digital health care services through online platforms, primarily due to language barriers and limited digital skills, with reliance on younger family members or community organizations for assistance; the lack of digital literacy among older community members was a prominent concern, and digital health care services were felt to be tailored for English speakers, with minimal consultation during the development phase), (2) importance of face-to-face (in-person) appointments for patient-clinician interactions (in-person appointments were strongly preferred, emphasizing the value of physical interaction and connection with health care professionals; video consultations were seen as an acceptable alternative), (3) stressors caused by the shift to remote access (the transition to remote digital access caused stress, fear, and anxiety; participants felt that digital health solutions were imposed without sufficient explanation or consent; and Black African and Caribbean participants reported experiences of racial discrimination within the health care system), and (4) digital solutions (evaluating technology acceptance; participants acknowledged the importance of digitalization but cautioned against viewing it as a one-size-fits-all solution; they advocated for offline alternatives and a hybrid approach, emphasizing the need for choice and a well-staffed clinical workforce). CONCLUSIONS: Digital health initiatives should address the digital divide, health inequalities, and the specific challenges faced by older adults, particularly those from minoritized ethnic backgrounds, ensuring accessibility, choice, and privacy. Overcoming language barriers involves more than mere translation. Maintaining in-person options for consultations, addressing sensitive issues, and implementing support systems at the practice level to support those struggling to access services are vital. This study recommends that policy makers ensure the inclusivity of older adults from diverse backgrounds in the design and implementation of digital health and social care services.
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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 | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».