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Bibliometric analysis of citizen science in global healthcare: Current trends, challenges, and future directions
- 1 Zhejiang Wanli University
* Author to whom correspondence should be addressed.
Abstract
With the development of information technologies such as big data, cloud computing, and artificial intelligence, the application scenarios of citizen science worldwide are no longer limited to natural science research. A large number of application cases have emerged in the field of medical health, which suggests that the development of citizen science may bring new impetus to medical health. This study employs CiteSpace for a bibliometric analysis to evaluate the current status, objectives, and methodologies of citizen science in global healthcare. An analysis of 248 articles from the Web of Science Core Collection reveals that citizen science is predominantly implemented in high-income countries, including the United States, the United Kingdom, Germany, and the Netherlands, with significant applications in public health, chronic disease management, infectious disease surveillance, and digital health. The COVID-19 pandemic has further catalyzed the integration of citizen science into health surveillance and public health responses. Conversely, in low- and middle-income countries (LMICs), the development and implementation of citizen science remain nascent, primarily concentrated in traditional domains such as environmental science, ecology, and biodiversity conservation, with limited penetration into the healthcare sector. Several structural barriers, including inadequate technological infrastructure, insufficient policy support, limited public engagement, and constrained resource allocation, have impeded the broader adoption of citizen science in these regions. To mitigate these challenges, this study proposes targeted strategies, these interventions are anticipated to facilitate the expansion of citizen science’s role in global health governance, ultimately contributing to improved public health outcomes and social welfare.
Keywords
citizen science, healthcare, bibliometric analysis, CiteSpace
[1]. Silvertown, J. (2009). A new dawn for citizen science. Trends in Ecology & Evolution, 24(9), 467–471. https://doi.org/10.1016/j.tree.2009.03.017
[2]. Kullenberg, C., & Kasperowski, D. (2016). What is citizen science? – A scientometric meta-analysis. PLOS ONE, 11(1), e0147152. https://doi.org/10.1371/journal.pone.0147152
[3]. Bonney, R., Cooper, C. B., Dickinson, J., et al. (2009). Citizen science: A developing tool for expanding science knowledge and scientific literacy. BioScience, 59(11), 977–984. https://doi.org/10.1525/bio.2009.59.11.9
[4]. McKinley, D. C., Miller-Rushing, A. J., Ballard, H. L., et al. (2017). Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation, 208, 15–28. https://doi.org/10.1016/j.biocon.2016.05.015
[5]. Follett, R., & Strezov, V. (2015). An analysis of citizen science based research: Usage and publication patterns. PLOS ONE, 10(11), e0143687. https://doi.org/10.1371/journal.pone.0143687
[6]. Laird, Y., Marks, L., Smith, B. J., et al. (2023). Harnessing citizen science in health promotion: Perspectives of policy and practice stakeholders in Australia. Health Promotion International, 38(5). https://doi.org/10.1093/heapro/daac003
[7]. Ballman, E. S., Leahy, J. E., Sponarski, C. C., et al. (2023). A citizen science approach to investigate the distribution, abundance, and pathogen infection of vector ticks through active surveillance. Ticks and Tick-borne Diseases, 14(3), 102144. https://doi.org/10.1016/j.ttbdis.2022.102144
[8]. Sgroi, G., D’Alessio, N., Marucci, G., et al. (2023). Trichinella britovi in wild boar meat from Italy, 2015–2021: A citizen science approach to surveillance. One Health, 16, 100480. https://doi.org/10.1016/j.onehlt.2023.100480
[9]. Ireson, J., Taylor, A., Richardson, E., et al. (2022). Exploring invisibility and epistemic injustice in Long Covid—A citizen science qualitative analysis of patient stories from an online Covid community. Health Expectations, 25(4), 1753–1765. https://doi.org/10.1111/hex.13511
[10]. Marks, L., Laird, Y., Trevena, H., et al. (2022). A scoping review of citizen science approaches in chronic disease prevention. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.805759
[11]. Tran, V.-T., Riveros, C., Péan, C., et al. (2019). Patients’ perspective on how to improve the care of people with chronic conditions in France: A citizen science study within the ComPaRe e-cohort. BMJ Quality & Safety, 28(11), 875–886. https://doi.org/10.1136/bmjqs-2019-009048
[12]. Winter, T., Riordan, B. C., Scarf, D., et al. (2022). Conspiracy beliefs and distrust of science predict reluctance of vaccine uptake of politically right-wing citizens. Vaccine, 40(12), 1896–1903. https://doi.org/10.1016/j.vaccine.2022.02.027
[13]. Fraisl, D., See, L., Estevez, D., et al. (2023). Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization’s Triple Billion Targets. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1131154
[14]. Odeyemi, E., Chesser, S., King, A. C., et al. (2023). Engaging Nigerian older persons in neighborhood environment assessment for physical activity participation: A citizen science project. Innovation in Aging, 8(4). https://doi.org/10.1093/geroni/igad002
[15]. Chen, Y., Chen, C., Liu, Z., et al. (2015). The methodological functions of CiteSpace knowledge mapping. Studies in Science of Science, 33(02), 242-253. https://doi.org/10.16192/j.cnki.1003-2053.2015.02.009
[16]. Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. PNAS, 101(suppl_1), 5303-5310. https://doi.org/10.1073/pnas.0307513100
[17]. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. https://doi.org/10.1002/asi.20317
[18]. Chen, C., Ibekwe-SanJuan, F., & Hou, J. (2010). The structure and dynamics of cocitation clusters: A multiple‐perspective cocitation analysis. Journal of the American Society for Information Science and Technology, 61(7), 1386-1409. https://doi.org/10.1002/asi.21309
[19]. Egghe, L. (2006). Theory and practice of the g-index. Scientometrics, 69(1), 131–152.
[20]. Chen, X. (2013). Research on the clustering analysis method based on minimum spanning tree (Doctoral dissertation). Chongqing University.
[21]. Harrison, J. A. (2020). Citizen science in South Africa: A personal perspective. OSTRICH, 91(2), 192-194. https://doi.org/10.2989/00306525.2020.1755381
[22]. Russell, C. (2021). Handbook of citizen science in ecology and conservation. African Journal of Range & Forage Science, 38(4), 296-297. https://doi.org/10.2989/10220119.2021.1938673
[23]. Taylor, J., Graham, M., Louw, A., Lepheana, A., Madikizela, B., Dickens, C., ... & Warner, S. (2022). Social change innovations, citizen science, miniSASS and the SDGs. Water Policy, 24(5), 708-717. https://doi.org/10.2166/wp.2021.264
[24]. Potts, W. M., Mann-Lang, J. B., Mann, B. Q., Griffiths, C. L., Attwood, C. G., de Blocq, A. D., ... & Thornycroft, R. (2021). South African marine citizen science - Benefits, challenges and future directions. African Journal of Marine Science, 43(3), 353-366. https://doi.org/10.2989/1814232X.2021.1960890
[25]. Amarakoon, V., Dharmarathne, G., Premasiri, R., Mukherjee, M., Shaw, R., & Wickramasinghe, D. (2024). Potential for the complementary and integrative use of citizen science and modern science in flood risk reduction: A case study from Sri Lanka. International Journal of Disaster Risk Reduction, 103, 104331. https://doi.org/10.1016/j.ijdrr.2024.104331
[26]. Callaghan, C. T., Mesaglio, T., Ascher, J. S., Brooks, T. M., Cabras, A. A., Chandler, M., ... & Young, A. N. (2022). The benefits of contributing to the citizen science platform iNaturalist as an identifier. PLOS Biology, 20(11), e3001843. https://doi.org/10.1371/journal.pbio.3001843
[27]. Khan, S., Hossain, F., Pavelsky, T., Parkins, G. M., Lane, M. R., Gomez, A. M., ... & Ahmad, S. K. (2023). Understanding volume estimation uncertainty of lakes and wetlands using satellites and citizen science. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 2386-2401. https://doi.org/10.1109/JSTARS.2023.3250354
[28]. Simpson, R., Page, K. R., & De Roure, D. (2014). Zooniverse. In Proceedings of the 23rd International Conference on World Wide Web. ACM.
[29]. Smolinski, M. S., Crawley, A. W., Baltrusaitis, K., et al. (2015). Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. American Journal of Public Health, 105(10), 2124–2130.
[30]. Wadi-Ramahi, S., Li, B., Waqqad, F., & AlSharif, A. (2023). Evolving practice in global healthcare: Remote physics support for low- and middle-income countries. Journal of Applied Clinical Medical Physics, 24(3). https://doi.org/10.1002/acm2.13914
[31]. Mwanza, J., Telukdarie, A., & Igusa, T. (2023). Impact of industry 4.0 on healthcare systems of low- and middle-income countries: A systematic review. Health and Technology, 13(1), 35-52. https://doi.org/10.1007/s12553-022-00714-2
[32]. Kakietek, J. J., Eberwein, J. D., Stacey, N., Newhouse, D., & Yoshida, N. (2022). Foregone healthcare during the COVID-19 pandemic: Early survey estimates from 39 low- and middle-income countries. Health Policy and Planning, 37(6), 771-778. https://doi.org/10.1093/heapol/czac024
[33]. Teixeira de Siqueira Filha, N., Li, J., Phillips-Howard, P. A., Quayyum, Z., Kibuchi, E., Mithu, M. I. H., ... & Elsey, H. (2022). The economics of healthcare access: A scoping review on the economic impact of healthcare access for vulnerable urban populations in low- and middle-income countries. International Journal for Equity in Health, 21(1). https://doi.org/10.1186/s12939-022-01804-3
[34]. De, P., & Pradhan, M. R. (2023). Effectiveness of mobile technology and utilization of maternal and neonatal healthcare in low- and middle-income countries (LMICs): A systematic review. BMC Women’s Health, 23(1). https://doi.org/10.1186/s12905-023-02825-y
[35]. Kwaitana, D., Chisoni, F., van Breevoort, D., Mildestvedt, T., Meland, E., Bates, J., & Umar, E. (2024). Primary healthcare service delivery for older people with progressive multimorbidity in low- and middle-income countries: A systematic review. Transactions of the Royal Society of Tropical Medicine and Hygiene, 118(3), 137-147. https://doi.org/10.1093/trstmh/trad068
[36]. Walters, S., Aldous, C., & Malherbe, H. (2024). Knowledge, attitudes, and practices of primary healthcare practitioners in low- and middle-income countries: A scoping review on genetics. Journal of Community Genetics. https://doi.org/10.1007/s12687-024-00721-y
[37]. Clarke-Deelder, E., Opondo, K., Oguttu, M., Burke, T., Cohen, J. L., & McConnell, M. (2023). Immediate postpartum care in low- and middle-income countries: A gap in healthcare quality research and practice. American Journal of Obstetrics & Gynecology MFM, 5(2), 100764. https://doi.org/10.1016/j.ajogmf.2022.100764
[38]. MacKechnie, M. C., Miclau, T. A., Cordero, D. M., Tahir, P., & Miclau, T., III. (2022). Leadership development programs for healthcare professionals in low-and middle-income countries: A systematic review. International Journal of Health Planning and Management, 37(4), 2149-2166. https://doi.org/10.1002/hpm.3457
[39]. Pradhan, N. A., Samnani, A. A. B. A., Abbas, K., & Rizvi, N. (2023). Resilience of primary healthcare system across low- and middle-income countries during COVID-19 pandemic: A scoping review. Health Research Policy and Systems, 21(1). https://doi.org/10.1186/s12961-023-01031-4
[40]. Ciecierski-Holmes, T., Singh, R., Axt, M., Brenner, S., & Barteit, S. (2022). Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: A systematic scoping review. NPJ Digital Medicine, 5(1). https://doi.org/10.1038/s41746-022-00700-y
[41]. Cragg, L., Williams, S., & Chavannes, N. H. (2016). FRESH AIR: An implementation research project funded through Horizon 2020 exploring the prevention, diagnosis and treatment of chronic respiratory diseases in low-resource settings. npj Primary Care Respiratory Medicine, 26(1). https://doi.org/10.1038/npjpcrm.2016.35
[42]. Killough, C. M., Madaras, A., Phillips, C., Hettema, J., Ceballos, V., Fuentes, J. E., ... & Page, K. (2023). Community health worker insights on promoting research engagement with diverse populations. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.959504
[43]. Undurraga, E. A., Carias, C., Meltzer, M. I., & Kahn, E. B. (2017). Potential for broad-scale transmission of Ebola virus disease during the West Africa crisis: Lessons for the Global Health Security Agenda. Infectious Diseases of Poverty, 6(1). https://doi.org/10.1186/s40249-017-0373-4
[44]. van Dongen, B. M., Ridder, M. A. M., Wolters, L., Steenhuis, I. H. M., & Renders, C. M. (2023). Increasing community capacity to improve the implementation of Health Promoting Schools: Barriers and facilitators from the FLASH intervention. Health Promotion International, 38(5). https://doi.org/10.1093/heapro/daad115
[45]. Dreisbach, C., Grayson, S., Leggio, K., Conway, A., & Koleck, T. (2022). Predictors of unrelieved symptoms in All of Us Research Program participants with chronic conditions. Journal of Pain and Symptom Management, 64(6), 555-566. https://doi.org/10.1016/j.jpainsymman.2022.08.018
[46]. de Kok, J. W. T. M., de la Hoz, M. Á. A., de Jong, Y., Brokke, V., Elbers, P. W. G., Thoral, P., ... & Borrat, X. (2023). A guide to sharing open healthcare data under the General Data Protection Regulation. Scientific Data, 10(1). https://doi.org/10.1038/s41597-023-02256-2
Cite this article
Feng,Y. (2024). Bibliometric analysis of citizen science in global healthcare: Current trends, challenges, and future directions. Journal of Clinical Technology and Theory,1,41-50.
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