Revisão sistemática dos estudos bibliométricos sobre SARS-CoV-2
Barra lateral de artigos
Publicado:
Set 24, 2020
Palavras-chave:
bibliometria, epidemia por 2019-nCoV, COVID-19, coronavirus, pesquisa
Conteúdo do artigo principal
Resumo
Objetivo: Realizar uma revisão sistemática de artigos que avaliaram a produção científica sobre SARS-CoV-2 por meio de análises bibliométricas. Métodos: Foram utilizados os bancos de dados Scopus, Web of Science e Google Scholar. Após a aplicação dos critérios de inclusão pré-estabelecidos, 30 artigos foram incluídos. Resultados. A quantidade total de artigos encontrados nos estudos bibliométricos sobre SARS-CoV-2 apresentou uma grande variação de 153 a 21.395 artigos e uma média igual a 4.279 (± 5.510). Um total de 17 países publicaram no escopo deste estudo, mas apenas seis publicaram mais de um artigo, com destaque para autores de instituições chinesas (17%). Scopus foi o banco de dados mais utilizado nos estudos bibliométricos (50%, n = 15). Os artigos usaram 72 palavras-chave diferentes com destaque para: COVID-19 (15%), SARS-CoV-2 (12%) e 2019-nCoV (9%).Conclusão. Estamos diante de um cenário sem precedentes de informações acerca do SARS-CoV-2 e isso tem exigido um esforço científico coletivo que se reflete na publicação diária de centenas de estudos (artigos, pré-impressões, guias clínicos, protocolos). Os métodos bibliométricos são sendo cada vez mais utilizados pela comunidade científica para sistematizar essas informações. Assim sendo, a revisão sistemática realizada nesse estudo permitiu fornecer uma visão geral da literatura bibliométrica sobre o vírus SARS-CoV-2.
Detalhes do artigo
Como Citar
1.
Silva TF, Melo AA de, Santos DLA dos, Vaz EC, Corvalan LCJ, Ribeiro M de L, Rodrigues FM. Revisão sistemática dos estudos bibliométricos sobre SARS-CoV-2. HSJ [Internet]. 24º de setembro de 2020 [citado 3º de julho de 2024];10(3):116-25. Disponível em: https://portalrcs.hcitajuba.org.br/index.php/rcsfmit_zero/article/view/1023
Seção
ARTIGO ORIGINAL
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3. Gorbalenya AE, Baker SC, Baric RS, et al. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol. 2020;5(4):536–44. https://doi.org/10.1038/s41564-020-0695-z
PMid:32123347
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7. Rinaldi B, Rinaldi JP. Available evidence on risk factors associated with COVID-19’s poorer outcomes, worldwide and in Brazil. Rev Cienc Saude. 2020;10(2):80–9. https://doi.org/10.21876/rcshci.v10i2.985
8. Bar-Ilan J. Citations to the “Introduction to informetrics” indexed by WOS, Scopus and Google Scholar. Scientometrics. 2010;82(3):495–506. https://doi.org/10.1007/s11192-010-0185-9
9. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097 PMCID: PMC2707599
10. Chahrour M, Assi S, Bejjani M, et al. A bibliometric analysis of Covid-19 research activity: A call for increased output. Cureus. 2020;12(3): e7357. https://doi.org/10.7759/cureus.7357 PMid: 32328369
11. Hamidah I, Sriyono S, Hudha MN. A Bibliometric analysis of Covid-19 research using VOSviewer. Indon J Sci Technol [Internet]. 2020 [cited 2020 Sep 22];5(2):34-41. https://doi.org/10.17509/ijost.v5i2.24522 Avaiable from: https://ejournal.upi.edu/index.php/ijost/article/view/24522
12. Melo MC, Cabral ERM, Rolim ACA, et al. A bibliometric analysis of global researches at COVID-19: COVID-19 bibliometric analysis. Interam J Med Health. 2020;3:e202003019. https://doi.org/10.31005/iajmh.v3i0.88
13. Kumar K. Author productivity of COVID-19 research output globally: Testing Lotka's Law. SSRN [preprint]. 2020 Apr 6:1-15. http://dx.doi.org/10.2139/ssrn.3603889
14. Dehghanbanadaki H, Seif F, Vahidi Y, et al. Bibliometric analysis of global scientific research on Coronavirus (COVID-19). Med J Islam Repub Iran [Internet]. 2020 [cited 2020 Sep 22];34:51. Avaiable from: http://mjiri.iums.ac.ir/article-1-6629-en.pdf
15. Lou J, Tian SJ, Niu SM, et al. Coronavirus disease 2019: a bibliometric analysis and review. Eur Rev Med Pharmacol Sci. 2020;24(6):3411-21. https://doi.org/10.26355/eurrev_202003_20712
16. Belli S, Mugnaini R, Baltà J, Abadal E. Coronavirus mapping in scientific publications: when science advances rapidly and collectively, is access to this knowledge open to society? Scientometrics. 2020;124:2661–85. https://doi.org/10.1007/s11192-020-03590-7
17. Liu N, Chee ML, Niu C, et al. Coronavirus disease 2019 (COVID-19): an evidence map of medical literature. BMC Med Res Methodol. 2020;20:177. https://doi.org/10.1186/s12874-020-01059-y
18. Haghani M, Bliemer MCJ. Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCov literature. bioRxiv [preprint]. 2020 Jun 1 [cited 2020 Sep 22];2006.00674. Available from: https://arxiv.org/abs/2006.00674
19. Pathak M. COVID-19 research in India: a quantitative analysis. Indian J Biochem Biophys. 2020;57(3):351–5. Available from: http://nopr.niscair.res.in/handle/123456789/54461
20. Kousha K, Thelwall M. COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts. Quant Sci Stud. 2020;1(3):1068-91. https://doi.org/10.1162/qss_a_00066
21. Fiesco-Sepúlveda KY, Serrano-Bermúdez LM. Contributions of Latin American researchers in the understanding of the novel coronavirus outbreak: a literature review. Peer J. 2020;8:e9332. https://doi.org/10.7717/peerj.9332 PMId: 32547890
22. Hossain MM. Current status of global research on novel coronavirus disease (COVID-19): a bibliometric analysis and knowledge mapping [version 1; peer review: 2 approved with reservations]. F1000Research 2020;9:374. https://doi.org/10.12688/f1000research.23690.1
23. Torres-Salinas D. Daily growth rate of scientific production on Covid-19. Analysis in databases and open access repositories. El Profes Inform. 2020;29(2): e290215. https://doi.org/10.3145/epi.2020.mar.15
24. Kirchhoff J, Mertens A, Scheufen M. Der Corona-Innovationswettlauf in der Wissenschaft: Eine Analyse der wissenschaftlichen Publikationen zur Bekämpfung der Corona-Pandemie und die Bedeutung für den Pharma-Standort Deutschland, IW-Report [Internet]. Köln: Institut der deutschen Wirtschaft (IW); 2020(17):30pp. Available from: http://hdl.handle.net/10419/216830
25. Hu YJ, Chen MM, Wang Q, et al. From SARS to COVID-19: A bibliometric study on emerging infectious diseases with natural language processing technologies. Research Square [preprint];2020 May 6 [cited 2020 Sep 22]. https://doi.org/10.21203/rs.3.rs-25354/v1
26. Helliwell JA, Bolton WS, Burke JR, Tiernan JP, Jayne DG, Chapman SJ. Global academic response to COVID-19: Cross-sectional study. medRxiv [preprint]. 2020 May 3 [cited 2020 Sep 22]. https://doi.org/10.1101/2020.04.27.20081414
27. Latif S, Usman M, Manzoor S, et al. Leveraging data science to combat COVID-19: A comprehensive review. TechRxiv [preprint]. 2020 Apr 30 [cited 2020 Sep 22]. https://doi.org/10.36227/techrxiv.12212516.v1
28. O’Brien N, Barboza-Palomino M, Ventura-León J, Caycho-Rodríguez T, Sandoval-Díaz JS, López-López W, et al. Coronavirus disease (COVID-19). A bibliometric analysis. Rev Chil Anest. 2020;49(3):408–15. https://doi.org/10.25237/revchilanestv49n03.020
29. Torres-Salinas D, Robinson-Garcia N, Castillo-Valdivieso PA. Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 related literature. BioRxiv [preprint]. 2020 Apr 26 [cited 2020 Sep 22]. https://doi.org/10.1101/2020.04.23.057307
30. Tran BX, Ha GH, Nguyen LH, et al. Studies of novel coronavirus disease 19 (COVID-19) pandemic: A global analysis of literature. Int J Environ Res Public Health. 2020;17(11):4095. https://doi.org/10.3390/ijerph17114095
31. Zhou Y, Chen L. Twenty-year span of global coronavirus research trends: a bibliometric analysis. In Int J Environ Res Public Health. 2020;17(9),3082. https://doi.org/10.3390/ijerph17093082
32. Haghani M, Bliemer MC, Goerlandt F, Li J. The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review. Safety Sci. 2020;129:104806. https://doi.org/10.1016/j.ssci.2020.104806
33. Aguado-Cortés C, Castaño VM. Translational knowledge map of COVID-19. arXiv [preprint]. 2020 Mar 22 [cited 2020 Sep 22]:2003.10434. Avaiable from: https://arxiv.org/abs/2003.10434
34. Gori AD, Boetto E, Fantini MP. Analysis of the scientific literature in the first 30 Days of the novel coronavirus outbreak. medRxiv [preprint]. 2020 Mar 30 [cited 2020 Sep 22]: 2020.03.25.20043315. https://doi.org/10.1101/2020.03.25.20043315
35. Golinelli D, Nuzzolese AG, Boetto E, et al. The impact of early scientific literature in response to COVID-19: a scientometric perspective. medRxiv [preprint]. 2020 Apr 18 [cited 2020 Sep 22]. https://doi.org/10.1101/2020.04.15.20066183
36. Kambhampati SB, Vaishya R, Vaish A. Unprecedented surge in publications related to COVID-19 in the first three months of pandemic: A bibliometric analytic report. J Clin Orthop Trauma. 2020;11(Suppl3):S304. https://doi.org/10.1016/j.jcot.2020.04.030
37. Bhattacharya S, Singh S. Visible Insights of the Invisible Pandemic: A Scientometric, Altmetric and Topic Trend Analysis. arXiv [preprint]. 2020 Apr 22[cited 2020 Sep 22]:arXiv:2004.10878. Avaiable from: https://arxiv.org/abs/2004.10878
38. Zhang L, Li B, Jia P, et al. [An analysis of global research on SARS-CoV-2]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020;37(2):236-45. Chinese. https://doi.org/10.7507/1001-5515.202002034. PMID: 32329275.
39. Rathbone J, Carter M, Hoffmann T, Glasziou P. A comparison of the performance of seven key bibliographic databases in identifying all relevant systematic reviews of interventions for hypertension. Syst Rev. 2016;5:27. https://doi.org/10.1186/s13643-016-0197-5 PMID: 26862061 PMCID: PMC4748526
40. Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: How will we ever keep up? PLoS Med. 2010;7(9):e1000326. https://doi.org/10.1371/journal.pmed.1000326 PMID:20877712
41. Cavacini A. What is the best database for computer science journal articles? Scientometrics. 2015;102(3):2059–71. https://doi.org/10.1007/s11192-014-1506-1
42. Freeman MK, Lauderdale SA, Kendrach MG, Woolley TW. Google scholar versus PubMed in locating primary literature to answer drug-related questions. Ann Pharmacother. 2009;43(3):478–84. https://doi.org/10.1345/aph.1L223 PMID:19261965
43. Berg JM, Bhalla N, Bourne PE, et al. Preprints for the life sciences. Science. 2016;352(6288):899-901 https://doi.org/10.1126/science.aaf9133
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