Assessing Public Perceptions of Virtual Primary Care During the Covid-19 Pandemic in the UK, Germany, Sweden, and Italy
A Topic Modeling Approach
Bibliographic Data
| ID | 16912427 |
|---|---|
| Authors | Felix Machleid (0000-0002-6597-4178, Humboldt-Universität zu Berlin, corresponding author), Roberto Fernández Crespo (0000-0002-2328-3782, Imperial College London), Kelsey Flott (0000-0002-7835-4322, Imperial College London), Saira Ghafur (0000-0003-4487-6904, Imperial College London), Ara Darzi (0000-0001-7815-7989, Imperial College London), Enrique Mayer (0000-0002-5509-4580, Imperial College London), Ana Luísa Neves (0000-0002-7107-7211, Imperial College London) |
| Year | 2024 |
| Volume | 14 |
| Issue | 3 |
| Publication date | 2024-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/21582440241263147 |
| OpenAlex | W4401651423 |
| Language | EN |
| References cited | 39 |
The COVID-19 pandemic has driven the transition from face-to-face visits to virtual care delivery. In this study, we explore patients’ perceptions of the benefits and challenges of using virtual primary care technologies during the pandemic, using machine learning approaches. A cross-sectional survey was conducted in August 2020 in Italy, Sweden, Germany, and the UK. Latent Dirichlet Allocation was used to identify themes of two open-ended questions. Comparisons between participant characteristics were made using Wilcoxon rank-sum test. 6,331 participants were included (51.7% female; 42.4% +55 years; 60.5% white ethnicity; 86.6% low literacy). The benefits extracted included: primary care delivery, infection control, reducing contacts, virtual care, timeliness, patient-doctor interaction, convenience, and safety. Participants from Sweden were most likely to mention “primary care delivery” (UK p = .007, IT p = .03, DE p
2019-20 coronavirus outbreak · Family medicine · Outbreak · Pandemic · Perception · Political science · Primary care · Data-Driven Disease Surveillance · Medicine · Psychology · Social Media in Health Education · Telemedicine and Telehealth Implementation · Virology
eHeals
Finding scientific topics
A density-based method for adaptive LDA model selection
On Finding the Natural Number of Topics with Latent Dirichlet Allocation
Strengthening the reporting of observational studies in epidemiology (STROBE) statement
Learning the parts of objects by non-negative matrix factorization
Telehealth transformation
Covid-19 risk perception
| Highly cited | No |
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