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Research on National Disaster Life Support Course in China

Published online by Cambridge University Press:  20 August 2019

Lujia Tang ,
Shuming Pan ,
Ying Chen ,
Hongmei Tang  and
Xuejing Li
Lujia Tang
Affiliation:
Emergency Department, Xinhua Hospital Affiliated to Shanghai Jiaotong University Shanghai, China
Shuming Pan
Affiliation:
Emergency Department, Xinhua Hospital Affiliated to Shanghai Jiaotong University Shanghai, China
Ying Chen
Affiliation:
School of Information Science and Technology, Sanda University, Shanghai, China
Hongmei Tang
Affiliation:
Clinical Medical College, Shanghai University of Medicine and Health Sciences, Shanghai, China
Xuejing Li*
Affiliation:
School of Information Science and Technology, Sanda University, Shanghai, China
*
Correspondence and reprint requests to Xuejing Li, School of Information Science and Technology, Sanda University, 2727 Jin Hai Road, Pu Dong District, Shanghai, China (E-mail: lxj05030@163.com)
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Abstract

Objectives:

To provide scientific, theoretical support for the improvement of medical disaster training, we systematically analyzed the National Disaster Life Support (NDLS) Course and established a training curriculum with feedback based on the current status of disaster medicine in China.

Methods:

The gray prediction model is applied to long-term forecast research on course effect. In line with the hypothesis, the NDLS course with feedback capability is more scientific and standardized.

Results:

The current training NDLS course system is suitable for Chinese medical disasters. After accepting the course training, audiences’ capabilities were enhanced. In the constructed GM (1,1) model prediction, the developing coefficients of the pretest and the posttest are 0.04 and 0.057, respectively. In light of the coefficient, the model is appropriate for the long-term prediction. The predicted results can be used as the basis for constructing training closed-loop optimization feedback. It can indicate that the course system has a good effect as well.

Conclusions:

According to the constructed GM model, the NDLS course system is scientific, practical, and operational. The research results can provide reference for relevant departments and be used for the construction of similar training course systems.

Keywords

disasterNDLStraining
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 14 , Issue 5 , October 2020 , pp. 577 - 584
Copyright
Copyright © 2019 Society for Disaster Medicine and Public Health, Inc.

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References

REFERENCES

Sorani, M, Tourani, S, Khankeh, HR, et al. Prehospital emergency medical services challenges in disaster: a qualitative study. Emerg (Tehran). 2018,6(1):e26.Google ScholarPubMed
Centre for Research in the Epidemiology of Disasters. The human cost of natural disasters 2015: a global perspective. http://emdat.Be/human_cost_natdis. Published June 26, 2015. Accessed May 6, 2018.Google Scholar
Wallemacq, P. CRED Crunch 48. Disaster data: a balanced perspective. Centre for Research on the Epidemiology of Disasters. 2017.Google Scholar
CRED and UNISDR. 2018 Review of Disaster Events. Brussels: CRED; 2019.Google Scholar
Wallemacq, P, Below, R, McLean, D. UNISDR and CRED report. Economic Losses, Poverty & Disasters (1998-2017). Brussels: CRED; 2018.Google Scholar
Coico, R, Kachur, E, Lima, V, et al. Guidelines for preclerkship bioterrorism curricula. Acad Med. 2004;79(4):366-375.CrossRefGoogle ScholarPubMed
Parrish, AR, Oliver, S, Jenkins, D, et al. A short medical school course on responding to bioterrorism and other disasters. Acad Med. 2005;80(9):820-823.CrossRefGoogle Scholar
Melnikov, S, Itzhaki, M, Kagan, I. Israeli nurses’ intention to report for work in an emergency or disaster. J Nurs Scholarsh. 2014;46(2):134-142.CrossRefGoogle ScholarPubMed
Sonneborn, O, Miller, C, Head, L, et al. Disaster education and preparedness in the acute care setting: a cross sectional survey of operating theatre nurse’s disaster knowledge and education. Nurse Educ Today. 2018;65:23-29.CrossRefGoogle ScholarPubMed
Scott, LA, Carson, DS, Greenwell, IB. Disaster 101: a novel approach to disaster medicine training for health professionals. J Emerg Med. 2010;39(2):220-226.CrossRefGoogle ScholarPubMed
Dennis, AJ, Brandt, MM, Steinberg, J, et al. Are general surgeons behind the curve when it comes to disaster preparedness training? A survey of general surgery and emergency medicine trainees in the United States by the Eastern Association for the Surgery for Trauma Committee on Disaster Preparedness. J Trauma Acute Care Surg. 2012;73(3):612-617.CrossRefGoogle ScholarPubMed
Gates, JD, Arabian, S, Biddinger, P, et al. The initial response to the Boston marathon bombing: lessons learned to prepare for the next disaster. Ann Surg. 2014;260(6):960-966.CrossRefGoogle ScholarPubMed
Schenk, E, Wijetunge, G, Mann, NC, et al. Epidemiology of mass casualty incidents in the United States. Prehosp Emerg Care. 2014;18(3):408-416.CrossRefGoogle ScholarPubMed
Djalali, A, Hosseinijenab, V, Hasani, A, et al. A fundamental, national, medical disaster management plan: an education-based model. Prehosp Disaster Med. 2009;24(6):565-569.CrossRefGoogle Scholar
Mackenzie, EJ, Rivara, FP, Jurkovich, GJ, et al. A national evaluation of trauma-center care on mortality. N Engl J Med. 2006;354(4):366-378.CrossRefGoogle ScholarPubMed
Swienton, R. Basic Disaster Life Support Course Manual. Augusta, GA: National Disaster Life Support Foundation (NDLSF); 3.0 ed. 2012:1-440 Google Scholar
Shultz, JM, Marcelin, LH, Madanes, SB, et al. The “Trauma Signature:” understanding the psychological consequences of the 2010 Haiti earthquake. Prehosp Disaster Med. 2011;26(5):353-366.CrossRefGoogle ScholarPubMed
Ingrassia, PL, Ragazzoni, L, Tengattini, M, et al. Nationwide program of education for undergraduates in the field of disaster medicine: development of a core curriculum blended learning and simulation tools. Prehosp Disaster Med. 2014;29:508-515.CrossRefGoogle ScholarPubMed
Wiesner, L, Kappler, S, Shuster, A, et al. Disaster training in 24 hours: evaluation of a novel medical student curriculum in disaster medicine. J Emerg Med. 2018;543(3):348-353.CrossRefGoogle Scholar
Schwartz, RB, Armstrong, J. Advanced Disaster Life Support Course Manual. Augusta, GA: National Disaster Life Support Foundation (NDLSF); 3.0 ed. 2011:1-264.Google Scholar
Chen, CI, Huang, SJ. The necessary and sufficient condition for GM (1, 1) grey prediction model. Appl Math Comput. 2013;219(11):6152-6162.Google Scholar
Hsu, CC, Chen, CY. Applications of improved grey prediction model for power demand forecasting. Energy Convers Manag. 2003;44(14):2241-2249.CrossRefGoogle Scholar
Kayacan, E, Ulutas, B, Kaynak, O. Grey system theory-based models in time series prediction. Expert Syst Appl. 2010;37(2):1784-1789.CrossRefGoogle Scholar
Wang, Y, Wei, F, Sun, C, et al. The Research of improved Grey GM (1, 1) model to predict the postprandial glucose in Type 2 diabetes. BioMed Res Int. 2016;2016:6837052.Google ScholarPubMed
Yang, X, Zou, J, Kong, D, et al. The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China. Medicine (Baltimore). 2018;97(34):e11787.CrossRefGoogle ScholarPubMed
Bobko, JP, Harris, WJ, Thomas, S. The first care provider system: improving community resilience for unexpected disasters. Why civilians should be prepared to act in mass-trauma events. EMS World. 2016;45(3):32, 34-38.Google ScholarPubMed
Zhong, S, Clark, M, Hou, XY, et al. Progress and challenges of disaster health management in china: a scoping review. Glob Health Action. 2014;7:24986.CrossRefGoogle ScholarPubMed