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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".