International Journal of Information Science and Management (IJISM)

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Peer Review Process

Journal Metrics

Open Access Policy

Language editing services

Bibliometric Analysis and Topic Modeling of Information Systems in Maternal Health Publications

Document Type : Articles

Authors

1 PhD Candidate in Medical Library and Information Science, Department of Medical library and Information Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.

2 Associate professor of Library and Information Science, Department of Medical library& Information Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

3 Associate professor of Medical Library and Information Sciences, Department of Medical library and Information Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.

4 PhD in Medical Library and Information Science, Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.

5 Assistant Professor of Medical Library and Information Science, Department of Medical library and Information Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.

Abstract

Due to the importance of maternal health for the development of society and the role of information systems in improving healthcare, this study aims to investigate and analyze the characteristics and topics of articles published in the field of information systems in maternal health. The articles were retrieved from the Web of Science (WoS) on October 23, 2021. The bibliometric indicators included the number of documents and citations, top journals, institutes, and countries. The co-authorship collaboration network of the countries was examined using Bibliometrix 3.1 package and VOSviewer software (ver. 1.6.17). In addition to bibliometric analysis, the related topic modelling was calculated with Non-Matrix Factorization (NMF) algorithm in Python programming language. Overall, 1140 original articles were published in the selected field in the WoS database within the years 1991-2021. The results demonstrated an ascending growth in the number of publications. The "The University of London", the "London School of Hygiene Tropical Medicine", and the "World Health Organization"  (WHO) contributed the most to this field orderly. Researchers from the USA with 372 (32.63%), Brazil with 267 (23.42%), and England with 150 (13.2%) documents had the most scientific collaboration on publishing in this regard. The USA and England had the most collaboration in 38 articles in the co-authorship network of countries. Based on topic modelling analysis, five topic clusters, including "maternal mortality", "child and infant mortality", "risk factors related to pregnancy and maternal health", "Geographic Information Systems (GISs)", and "data quality in Health Information Systems (HISs)" were considered for this research. According to the research results, it can be concluded that there is a rising trend in the number of articles published in the field of information systems in maternal health. The USA, Brazil, and England have played a prominent role in scientific production in this regard. Given that this study gives a snapshot of the current status of the research topic and visualizes the collaboration between countries, the obtained results can guide future collaboration and encourage scientific institutes to expand their interactions.

Keywords

References
  1. Abdullah, S. S., Rahmat, M. I. Y., Ariffin, A., Rahim, S. A., Jamalludin, N. M. & Wahab, N. A. (2022). Exploring the structure and trends of research on single mother: a bibliometrics analysis. Journal of Global Business and Social Entrepreneurship (GBSE), 7(23). Retrived from http://myscholar.umk.edu.my/bitstream/123456789/4268/1/Paper-285-.pdf
  2. Ahmadian, L., Salehi, F. & Bahaadinbeigy, K. (2020). Application of geographic information systems in maternal health: A scoping review. Eastern Mediterranean Health Journal, 26(11), 1403-1414. https://doi.org/10.26719/emhj.20.095
  3. Aria, M. & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. . https://doi.org/https://doi.org/10.1016/j.joi.2017.08.007
  4. Babamohamadi, H., Jangjo, M., Nejat, M. & Kahouei, M. (2016). Exploring the effectiveness of obstetrics and gynecology information systems in hospitals of a developing country: A qualitative content analysis. International Journal of Medical Research Health Sciences, 5(7), 554-563. Retieved from https://www.ijmrhs.com/medical-research/exploring-the-effectiveness-of-obstetrics-and-gynecology-information-systems-in-hospitals-of-a-developing-country-a-qual.pdf
  5. Bazm, S., Kalantar, S. M. & Mirzaei, M. (2016). Bibliometric mapping and clustering analysis of Iranian papers on reproductive medicine in Scopus database (2010-2014). International Journal of Reproductive BioMedicine, 14(6), 371-382. PMID: 27525320; PMCID: PMC4971550
  6. Chen, X. & Xie, H. (2020). A Structural topic modeling-based bibliometric study of sentiment analysis literature. Cognitive Computation, 12(6), 1097-1129. . https://doi.org/10.1007/s12559-020-09745-1
  7. Chen, Y., Zhang, H., Liu, R., Ye, Z. & Lin, J. (2019). Experimental explorations on short text topic mining between LDA and NMF based Schemes. Knowledge-Based Systems, 163, 1-13. . https://doi.org/10.1016/j.knosys.2018.08.011
  8. Cho, K. W., Kim, S. Y. & Woo, Y. W. (2019). Analysis of women's health online news articles using topic modeling. Osong Public Health Research Perspectives, 10(3), 158-169. https://doi.org/10.24171/j.phrp.2019.10.3.07
  9. Dai, L., Zhang, N., Rong, L. & Ouyang, Y. Q. (2020). Worldwide research on fear of childbirth: A bibliometric analysis. PLOS ONE, 15(7), . https://doi.org/10.1371/journal.pone.0236567
  10. Frøen, J.F., Myhre, S.L., Frost, M.J., Chou, D., Mehl, G., Say, L., Cheng, S., Fjeldheim, I., Friberg, I.K., French, S. & Jani, J.V. (2016). eRegistries: Electronic registries for maternal and child health. BMC Pregnancy and Childbirth, 16(1), 1-15. . https://doi.org/10.1186/s12884-016-0801-7
  11. Hernández-Vásquez, A., Bendezu-Quispe, G., Comandé, D. & Gonzales-Carillo, O. (2020). Worldwide original research production on maternal near-miss: A 10-year bibliometric study. Revista Brasileira de Ginecologia e Obstetrícia, 42(10), 614-620. . https://doi.org/10.1055/s-0040-1715136
  12. Kihuba, E., Gathara, D., Mwinga, S., Mulaku, M., Kosgei, R., Mogoa, W., Nyamai, R. & English, M. (2014). Assessing the ability of health information systems in hospitals to support evidence-informed decisions in Kenya. Global Health Action, 7(1), 24859. . https://doi.org/10.3402/gha.v7.24859
  13. Koblinsky, M., Moyer, C.A., Calvert, C., Campbell, J., Campbell, O.M., Feigl, A.B., Graham, W.J., Hatt, L., Hodgins, S., Matthews, Z. & McDougall, L (2016). Quality maternity care for every woman, everywhere: A call to action. The Lancet, 388(10057), 2307-2320. https://doi.org/10.1016/S0140-6736(16)31333-2
  14. Le Meur, N., Gao, F. & Bayat, S. (2015). Mining care trajectories using health administrative information systems: the use of state sequence analysis to assess disparities in prenatal care consumption. BMC Health Services Research, 15(1), . https://doi.org/10.1186/s12913-015-0857-5
  15. Lebrun-Harris, L.A., Parasuraman, S.R., Norton, C., Livinski, A.A., Ghandour, R., Blumberg, S.J. & Kogan, M.D. (2021). Bibliometric analysis of research studies based on federally funded children's health surveys. Academic pediatrics, 21(3), 462-470. https://doi.org/10.1016/j.acap.2020.08.004
  16. Li, K., Rollins, J. & Yan, E. (2018). Web of Science use in published research and review papers 1997–2017: A selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115(1), 1-20 . https://doi.org/10.1007/s11192-017-2622-5
  17. Lopes, N. & Ribeiro, B. (2015). Non-Negative Matrix Factorization (NMF). In Machine Learning for Adaptive Many-Core Machines - A Practical Approach (pp. 127-154). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-06938-8_7
  18. Lund, S., Hemed, M., Nielsen, B.B., Said, A., Said, K., Makungu, M.H. & Rasch, V., (2012). Mobile phones as a health communication tool to improve skilled attendance at delivery in Zanzibar: a cluster‐randomised controlled trial. BJOG: An International Journal of Obstetrics & Gynaecology, 119(10), 1256-1264. . https://doi.org/10.1111/j.1471-0528.2012.03413.x
  19. Manning, C. D., Raghavan, P. & Schütze, H. (2008). Introduction to Information Retrieval: Cambridge University Press Cambridge.
  20. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. & Vanderplas, J., (2011). Scikit-learn: Machine learning in Python. the Journal of Machine Learning Research, 12, 2825-2830.
  21. Shi, T., Kang, K., Choo, J. & Reddy, C. K. (2018). Short-text topic modeling via Non-negative matrix factorization enriched with local word-context correlations. In Proceedings of the 2018 World Wide Web Conference, Lyon, France. https://doi.org/10.1145/3178876.3186009
  22. Song, H., May, A., Vaidhyanathan, V., Cramer, E. M., Owais, R. W. & McRoy, S. (2013). A two-way text-messaging system answering health questions for low-income pregnant women. Patient Education and Counseling, 92(2), 182-187. https://doi.org/10.1016/j.pec.2013.04.016
  23. Souza, D. R. S., de Morais, T. N. B., da Silva Costa, K. T., & de Andrade, F. B. (2021). Maternal health indicators in Brazil: A time series study. Medicine, 100(44). https://doi.org/10.1097/MD.0000000000027118
  24. van Eck, N. J. & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
  25. Visser, M., van Eck, N. J. & Waltman, L. (2021). Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic. Quantitative Science Studies, 2(1), 20-41. https://doi.org/10.1162/qss_a_00112 %J Quantitative Science Studies
  26. Wager, K. A., Lee, F. W. & Glaser, J. P. (2017). Health care information systems: A practical approach for health care management. United States of America: San Francisco: John Wiley & Sons.
  27. Wang, Y., Shan, C., Tian, Y., Pu, C. & Zhu, Z. (2021). Bibliometric analysis of global research on perinatal palliative care. Front Pediatr, 9, . https://doi.org/10.3389/fped.2021.827507
  28. Whitten, J. L. & Bentley, L. D. (2007). Systems analysis and design methods. New York, The United States: McGraw-Hill
  29. Xie, Y., Lang, D., Lin, S., Chen, F., Sang, X., Gu, P., Wu, R., Li, Z., Zhu, X. & Ji, L., (2021). Mapping maternal health in the new media environment: A scientometric analysis. International Journal of Environmental Research and Public Health, 18(24), 13095. . https://doi.org/10.3390/ijerph182413095
  30. Yuan, N., Wang, L., Li, Z. & Zhang, X. (2022). Thyroid diseases during pregnancy: bibliometric analysis of scientific publications. Endocrine, Metabolic & Immune Disorders - Drug Targets, 22(2), 247-258. . https://doi.org/10.2174/1871530321666210203214142

 

 

International Journal of Information Science and Management (IJISM)
Volume 21, Issue 2 - Serial Number 2
April 2023
Pages 85-101
Files
History
  • Receive Date: 26 April 2022
  • Revise Date: 31 January 2023
  • Accept Date: 07 January 2023
Share
How to cite
Statistics