IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v14y2024i3p21582440241263147.html
   My bibliography  Save this article

Assessing Public Perceptions of Virtual Primary Care During the COVID-19 Pandemic in the UK, Germany, Sweden, and Italy: A Topic Modeling Approach

Author

Listed:
  • Felix Machleid
  • Roberto Fernandez Crespo
  • Kelsey Flott
  • Saira Ghafur
  • Ara Darzi
  • Erik Mayer
  • Ana Luisa Neves

Abstract

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  

Suggested Citation

  • Felix Machleid & Roberto Fernandez Crespo & Kelsey Flott & Saira Ghafur & Ara Darzi & Erik Mayer & Ana Luisa Neves, 2024. "Assessing Public Perceptions of Virtual Primary Care During the COVID-19 Pandemic in the UK, Germany, Sweden, and Italy: A Topic Modeling Approach," SAGE Open, , vol. 14(3), pages 21582440241, August.
    • Handle: RePEc:sae:sagope:v:14:y:2024:i:3:p:21582440241263147
    DOI: 10.1177/21582440241263147
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440241263147
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440241263147?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Claudia R. Schneider & Sarah Dryhurst & John Kerr & Alexandra L. J. Freeman & Gabriel Recchia & David Spiegelhalter & Sander van der Linden, 2021. "COVID-19 risk perception: a longitudinal analysis of its predictors and associations with health protective behaviours in the United Kingdom," Journal of Risk Research, Taylor & Francis Journals, vol. 24(3-4), pages 294-313, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rafael Teixeira & Mário Antunes & Diogo Gomes & Rui L. Aguiar, 2024. "Comparison of Semantic Similarity Models on Constrained Scenarios," Information Systems Frontiers, Springer, vol. 26(4), pages 1307-1330, August.
    2. Del Corso, Gianna M. & Romani, Francesco, 2019. "Adaptive nonnegative matrix factorization and measure comparisons for recommender systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 164-179.
    3. P Fogel & C Geissler & P Cotte & G Luta, 2022. "Applying separative non-negative matrix factorization to extra-financial data," Working Papers hal-03689774, HAL.
    4. Xiao-Bai Li & Jialun Qin, 2017. "Anonymizing and Sharing Medical Text Records," Information Systems Research, INFORMS, vol. 28(2), pages 332-352, June.
    5. Naiyang Guan & Lei Wei & Zhigang Luo & Dacheng Tao, 2013. "Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    6. Spelta, A. & Pecora, N. & Rovira Kaltwasser, P., 2019. "Identifying Systemically Important Banks: A temporal approach for macroprudential policies," Journal of Policy Modeling, Elsevier, vol. 41(1), pages 197-218.
    7. M. Moghadam & K. Aminian & M. Asghari & M. Parnianpour, 2013. "How well do the muscular synergies extracted via non-negative matrix factorisation explain the variation of torque at shoulder joint?," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 16(3), pages 291-301.
    8. Markovsky, Ivan & Niranjan, Mahesan, 2010. "Approximate low-rank factorization with structured factors," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3411-3420, December.
    9. Paul Fogel & Yann Gaston-Mathé & Douglas Hawkins & Fajwel Fogel & George Luta & S. Stanley Young, 2016. "Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health," IJERPH, MDPI, vol. 13(5), pages 1-14, May.
    10. Le Thi Khanh Hien & Duy Nhat Phan & Nicolas Gillis, 2022. "Inertial alternating direction method of multipliers for non-convex non-smooth optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 247-285, September.
    11. Zhaoyu Xing & Yang Wan & Juan Wen & Wei Zhong, 2024. "GOLFS: feature selection via combining both global and local information for high dimensional clustering," Computational Statistics, Springer, vol. 39(5), pages 2651-2675, July.
    12. Chae, Bongsug (Kevin), 2018. "The Internet of Things (IoT): A Survey of Topics and Trends using Twitter Data and Topic Modeling," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190376, International Telecommunications Society (ITS).
    13. Md Nazrul Islam & Md Mofazzal Hossain & Md Shafayet Shahed Ornob, 2024. "Business research on Industry 4.0: a systematic review using topic modelling approach," Future Business Journal, Springer, vol. 10(1), pages 1-15, December.
    14. Jina Choo & Sooyeon Park & Songwhi Noh, 2021. "Associations of COVID-19 Knowledge and Risk Perception with the Full Adoption of Preventive Behaviors in Seoul," IJERPH, MDPI, vol. 18(22), pages 1-14, November.
    15. Jingfeng Guo & Chao Zheng & Shanshan Li & Yutong Jia & Bin Liu, 2022. "BiInfGCN: Bilateral Information Augmentation of Graph Convolutional Networks for Recommendation," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    16. Jianfei Cao & Han Yang & Jianshu Lv & Quanyuan Wu & Baolei Zhang, 2023. "Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    17. Wang, Ketong & Porter, Michael D., 2018. "Optimal Bayesian clustering using non-negative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 395-411.
    18. Lei, Da & Cheng, Long & Wang, Pengfei & Chen, Xuewu & Zhang, Lin, 2024. "Identifying service bottlenecks in public bikesharing flow networks," Journal of Transport Geography, Elsevier, vol. 116(C).
    19. Alfonso Gastelum-Strozzi & Claudia Infante-Castañeda & Juan Guillermo Figueroa-Perea & Ingris Peláez-Ballestas, 2021. "Heterogeneity of COVID-19 Risk Perception: A Socio-Mathematical Model," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
    20. Semi Min & Juyong Park, 2019. "Modeling narrative structure and dynamics with networks, sentiment analysis, and topic modeling," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-20, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:14:y:2024:i:3:p:21582440241263147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.