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Knowledge Maps of Disaster Medicine in China Based on Co-Word Analysis

Published online by Cambridge University Press:  23 July 2018

Wei Wei ,
Jie Ge ,
Sha Xu ,
Ming Li ,
Zhe Zhao ,
Xiaoxue Li  and
Jingchen Zheng
Wei Wei
Affiliation:
School of Public Health, Fudan University, Shanghai, China Institute of Modern Hospital Management, General Hospital of Chinese People’s Armed Police Forces, Beijing, China
Jie Ge
Affiliation:
Institute of Modern Hospital Management, General Hospital of Chinese People’s Armed Police Forces, Beijing, China Institute of Hospital Management, Tsinghua University, Beijing, China
Sha Xu
Affiliation:
Institute of Modern Hospital Management, General Hospital of Chinese People’s Armed Police Forces, Beijing, China
Ming Li
Affiliation:
Medical Department, General Hospital of Chinese People’s Armed Police Forces, Beijing, China
Zhe Zhao
Affiliation:
Institute of Modern Hospital Management, General Hospital of Chinese People’s Armed Police Forces, Beijing, China
Xiaoxue Li
Affiliation:
Institute of Modern Hospital Management, General Hospital of Chinese People’s Armed Police Forces, Beijing, China
Jingchen Zheng*
Affiliation:
School of Public Health, Fudan University, Shanghai, China Institute of Modern Hospital Management, General Hospital of Chinese People’s Armed Police Forces, Beijing, China
*
Correspondence and reprint requests to Jingchen Zheng, General Hospital of Chinese People’s Armed Police Forces, Haidian District, Beijing, China 100039 (e-mail: [email protected]).
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Abstract

Objective

We analyzed research themes in the field of disaster medicine in China to provide references for researchers to understand the research status and developing trends of this field.

Methods

Published journal articles were retrieved. A social network analysis was conducted to visualize the relations of high-frequency key words. A cluster analysis was used to classify key words. A strategic diagram analysis was conducted to visualize clusters across the entire research field.

Results

We retrieved 3,079 articles, from which 1,749 articles and 8,284 key words were identified after screening. High-frequency key words were classified into 6 clusters. “Medical rescue” had the highest degree and betweenness centralities. Cluster 4 was located in Quadrant I of the strategic diagrams.

Conclusions

“Medical rescue” is the core key word, and it serves a pivotal “bridge” function. “Emergencies” and similar terms are key words with special statuses. “Natural disaster medical rescue” and “fundamental theories of disaster medicine” constitute the primary and secondary core themes, respectively. “On-site emergency treatment techniques” is a marginalized theme. The other themes are emerging themes that offer considerable scope for future development. Generally, the scope and depth of investigations in this field should be improved. (Disaster Med Public Health Preparedness. 2019;13:405-409)

Keywords

cluster analysisco-word analysisdisaster medicinesocial network analysisstrategic diagram analysis
Type
Brief Report
Information
Disaster Medicine and Public Health Preparedness , Volume 13 , Issue 3 , June 2019 , pp. 405 - 409
Copyright
Copyright © 2018 Society for Disaster Medicine and Public Health, Inc. 

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References

REFERENCES

1. Davis, LB, Samanlioglu, F, Qu, X, Root, S. Inventory planning and coordination in disaster relief efforts. Int J Prod Econ. 2013;141(2):561-573.Google Scholar
2. Lennquist, S. Education and training in disaster medicine – time for a scientific approach. Int J Disaster Med. 2003;1(1):9-12.Google Scholar
3. Zhang, X, Yi, L, Zhao, D. Community-based disaster management: a review of progress in China. Nat Hazards. 2013;65(3):2215-2239.Google Scholar
4. Hou, S, Lv, Q, Ding, H, et al. Disaster medicine in China: present and future. Disaster Med Public. 2016;10:1-9.Google Scholar
5. Sweileh, WM, Aljabi, SW, Abutaha, AS, et al. Bibliometric analysis of worldwide scientific literature in mobile – health: 2006-2016. BMC Med Inform Decis. 2017;17:72.Google Scholar
6. Wang, Q, Sheng, Y. The current status and hot research topics in disaster nursing: a bibliometric analysis. J Nurs Sci. 2016;31(19):103-106.Google Scholar
7. Gong, B, Mohammed, MF, Nicolaou, S, et al. Diagnostic imaging in disasters: a bibliometric analysis. Disaster Med Public. 2017;32(S1):1-13.Google Scholar
8. Hossain, L, Karimi, F, Wigand, RT. Dynamics of a global zoonotic research network over 33 years (1980-2012). Disaster Med Public. 2015;9(5):496-503.Google Scholar
9. Fu, H, Hu, T, Wang, J, et al. A bibliometric analysis of malaria research in China during 2004-2014. Malaria J. 2015;14(1):1-7.Google Scholar
10. Mryglod, O, Holovatch, Y, Kenna, R, Berche, B. Quantifying the evolution of a scientific topic: reaction of the academic community to the Chornobyl disaster. Scientometrics. 2016;106(3):1151-1166.Google Scholar
11. Zong, QJ, Shen, HZ, Yuan, QJ, et al. Doctoral dissertations of library and information science in China: a co-word analysis. Scientometrics. 2013;94(2):781-799.Google Scholar
12. Liu, GY, Hu, JM. A co-word analysis of digital library field in China. Scientometrics. 2012;91(1):203-217.Google Scholar
13. Price, DJDS. Little science, big science – and beyond. Q Rev Biol. 1963;7(4):443-458.Google Scholar
14. Casella, G, Girón, FJ. Cluster analysis, model selection, and prior distributions on models. Bayesian Anal. 2014;9(3):613-658.Google Scholar
15. Yang, Y, Wu, M, Cui, L. Integration of three visualization methods based on co-word analysis. Scientometrics. 2012;90(2):659-673.Google Scholar
16. Lurie, SJ, Fogg, TT, Dozier, AM. Social network analysis as a method of assessing institutional culture: three case studies. Acad Med J Assoc Am Med Coll. 2009;84(8):1029-1035.Google Scholar
17. Burt, RS, Kilduff, M, Tasselli, S. Social network analysis: foundations and frontiers on advantage. Annu Rev Psychol. 2013;64(1):527-547.Google Scholar
18. Ghosh, R, Lerman, K. Rethinking centrality: the role of dynamical processes in social network analysis. Discrete Cont Dyn-B. 2014;19(5):1355-1372.Google Scholar
19. Gan, C, Wang, W. Research characteristics and status on social media in China: a bibliometric and co-word analysis. Scientometrics. 2015;105(2):1167-1182.Google Scholar
20. Coulter, N, Monarch, I, Konda, S. Software engineering as seen through its research literature: a study in co-word analysis. J Am Soc Inform Sci. 1998;49(13):1206-1223.Google Scholar
21. Khasseh, AA, Soheili, F, Moghaddam, HS, et al. Intellectual structure of knowledge in iMetrics: a co-word analysis. Inform Process Manag. 2017;53(3):705-720.Google Scholar