Sustainable Training-Model Development Based on Analysis of Disaster Medicine Training

Shoichi Ohta; Munekazu Takeda; Ryo Sasaki; Hirotaka Uesugi; Hironobu Kamagata; Kentaro Kawai; Satomi Kuroshima; Michie Kawashima; Masaki Onishi; Ikushi Yoda

doi:10.20965/jdr.2015.p0900

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JDR Vol.10 No.5 pp. 900-918

(2015)

doi: 10.20965/jdr.2015.p0900

Paper:

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Sustainable Training-Model Development Based on Analysis of Disaster Medicine Training

Shoichi Ohta^1, Munekazu Takeda^2, Ryo Sasaki^3, Hirotaka Uesugi^1, Hironobu Kamagata^1, Kentaro Kawai^1, Satomi Kuroshima^4, Michie Kawashima^5, Masaki Onishi^6, and Ikushi Yoda^6

^*1Department of Emergency and Critical Care Medicine, Tokyo Medical University
6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo 160-0023, Japan

^*2Tokyo Women's Medical University, Tokyo, Japan

^*3Center Hospital of the National Center for Global Health and Medicine, Tokyo, Japan

^*4Japan Society for the Promotion of Science / Chiba University, Chiba, Japan

^*5Kansai Gaidai College, Osaka, Japan

^*6National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan

Received:

May 15, 2015

Accepted:

August 25, 2015

Published:

October 1, 2015

Keywords:

disaster medical training, training model, disaster medicine, communication, triage

Abstract

In the Shinjuku Station West Exit Medical Relief Training, a disaster medical (triage) training that includes ordinary citizens as well as medical professionals has been conducted on a continual basis. However, updating and improving the training contents and maintaining the participants’ interest levels were challenged because there were no baseline evaluations on post-training accomplishments. The purpose of this study is to develop a training model which facilitate updates to the training contents in a sustainable manner and increase the number of participants by raising satisfaction levels. Peer evaluations and self-evaluations were introduced into the training framework to develop a training model that can be sustainably improved using scientific evaluation methods. The term “scientific” refers to introducing scientific methods to analyze training drills to increase the quantitative measurement of the participants’ post-training evaluations. This paper reports on the results of the actual implementation of the training model.

Cite this article as:

S. Ohta, M. Takeda, R. Sasaki, H. Uesugi, H. Kamagata, K. Kawai, S. Kuroshima, M. Kawashima, M. Onishi, and I. Yoda, “Sustainable Training-Model Development Based on Analysis of Disaster Medicine Training,” J. Disaster Res., Vol.10 No.5, pp. 900-918, 2015.

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References

[1] I. Kobayashi et al., “Development of Risk Management Training System for Natural Disaster,” Proceedings of Infrastructure Planning, Vol.18, pp. 245-254, 2001 (in Japanese).
[2] R. Kimura et al., “Training as Practical Education for Disaster Management in Nagoya University,” Journal of Japan Society of Civil Engineers, Vol.1, pp. 49-54, 2006 (in Japanese).
[3] K. Egawa et al., “Study on floor area required in the case of accepting many casualties,” Journal of Japanese Association for Disaster Medicine, Vol.16, No.1, pp. 88-92, 2011 (in Japanese).
[4] S. Ohta et al., “Evidence-based effective triage operation during disaster: application of human-trajectory data to triage drill sessionsi,” Prehospital and Disaster Medicine Vol.30, No.1, pp. 102-109, 2015.
[5] Advanced Life Support Group, “Major Incident Medical Management and Support: The Practical Approach at the Scene, 3^rd Edition,” BMJ Books, 2011.
[6] K. Egawa, I. Yoda, S. Ohta, and T. Yukioka, “Study for the Place of First-aid Stations in Tokyo Metropolitan Area,” The 12th Asia Pacific Conf. on Disaster Medicine, pp. 128, 2014.
[7] I. Yoda et al., “Developing scientific analysis system for the utilization of triage post: A case of medical disaster drill session suing 3D camera,” The 12^th Asia Pacific Conf. Disaster Medicine, pp. 103, 2014.
[8] R. Likert, “A Technique for the Measurement of Attitudes,” Archives of Psychology, Vol.140, pp. 55, 1932.
[9] I. Yoda and K. Sakaue, “Ubiquitous Stereo Vision for Human Sensing,” “Intelligent Environments: Methods, Algorithms and Applications,” Springer, pp. 91-108, October, 2008.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] I. Kobayashi et al., “Development of Risk Management Training System for Natural Disaster,” Proceedings of Infrastructure Planning, Vol.18, pp. 245-254, 2001 (in Japanese).

[2] [2] R. Kimura et al., “Training as Practical Education for Disaster Management in Nagoya University,” Journal of Japan Society of Civil Engineers, Vol.1, pp. 49-54, 2006 (in Japanese).

[3] [3] K. Egawa et al., “Study on floor area required in the case of accepting many casualties,” Journal of Japanese Association for Disaster Medicine, Vol.16, No.1, pp. 88-92, 2011 (in Japanese).

[4] [4] S. Ohta et al., “Evidence-based effective triage operation during disaster: application of human-trajectory data to triage drill sessionsi,” Prehospital and Disaster Medicine Vol.30, No.1, pp. 102-109, 2015.

[5] [5] Advanced Life Support Group, “Major Incident Medical Management and Support: The Practical Approach at the Scene, 3^rd Edition,” BMJ Books, 2011.

[6] [6] K. Egawa, I. Yoda, S. Ohta, and T. Yukioka, “Study for the Place of First-aid Stations in Tokyo Metropolitan Area,” The 12th Asia Pacific Conf. on Disaster Medicine, pp. 128, 2014.

[7] [7] I. Yoda et al., “Developing scientific analysis system for the utilization of triage post: A case of medical disaster drill session suing 3D camera,” The 12^th Asia Pacific Conf. Disaster Medicine, pp. 103, 2014.

[8] [8] R. Likert, “A Technique for the Measurement of Attitudes,” Archives of Psychology, Vol.140, pp. 55, 1932.

[9] [9] I. Yoda and K. Sakaue, “Ubiquitous Stereo Vision for Human Sensing,” “Intelligent Environments: Methods, Algorithms and Applications,” Springer, pp. 91-108, October, 2008.

Sustainable Training-Model Development Based on Analysis of Disaster Medicine Training

Shoichi Ohta*1, Munekazu Takeda*2, Ryo Sasaki*3, Hirotaka Uesugi*1, Hironobu Kamagata*1, Kentaro Kawai*1, Satomi Kuroshima*4, Michie Kawashima*5, Masaki Onishi*6, and Ikushi Yoda*6

Shoichi Ohta^1, Munekazu Takeda^2, Ryo Sasaki^3, Hirotaka Uesugi^1, Hironobu Kamagata^1, Kentaro Kawai^1, Satomi Kuroshima^4, Michie Kawashima^5, Masaki Onishi^6, and Ikushi Yoda^6