Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches

Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches Publisher

H Farhat HASSAN ; Am Batt Alan MICHAEL ; Mc Helou Mariana CHARBEL ; H Shin HEEJUN ; J Laughton JAMES ; C Dumbeck CAROLYN ; A Dehghani AREZOO ; F Rezaei FATEMEH ; Na Bajow Nidaa A ; Ljm Mortelmans Luc JM

Source: Journal of Contingencies and Crisis Management Published:2025

Abstract

Delphi studies in disaster medicine lack consensus on expert agreement metrics. This study examined various metrics using a Delphi study on chemical, biological, radiological, and nuclear (CBRN) preparedness in the Middle East and North Africa region. Forty international disaster medicine experts evaluated 133 items across ten CBRN Preparedness Assessment Tool themes using a 5-point Likert scale. Agreement was measured using Kendall's W, Intraclass Correlation Coefficient, and Cohen's Kappa. Statistical and machine learning techniques compared metric performance. The overall agreement mean score was 4.91 ± 0.71, with 89.21% average agreement. Kappa emerged as the most sensitive metric in statistical and machine learning analyses, with a feature importance score of 168.32. The Kappa coefficient showed variations across CBRN PAT themes, including medical protocols, logistics, and infrastructure. The integrated statistical and machine learning approach provides a promising method for understanding expert consensus in disaster preparedness, with potential for future refinement by incorporating additional contextual factors. © 2025 Elsevier B.V., All rights reserved.

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Style	Citing Format
MLA	H Farhat HASSAN, et al.. "Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches." Journal of Contingencies and Crisis Management, vol. 33, no. 2, 2025, pp. -.
APA	H Farhat HASSAN, Am Batt Alan MICHAEL, Mc Helou Mariana CHARBEL, H Shin HEEJUN, J Laughton JAMES, C Dumbeck CAROLYN, A Dehghani AREZOO, F Rezaei FATEMEH, Na Bajow Nidaa A, Ljm Mortelmans Luc JM (2025). Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches. Journal of Contingencies and Crisis Management, 33(2), -.
Chicago	H Farhat HASSAN, Am Batt Alan MICHAEL, Mc Helou Mariana CHARBEL, H Shin HEEJUN, J Laughton JAMES, C Dumbeck CAROLYN, A Dehghani AREZOO, F Rezaei FATEMEH, Na Bajow Nidaa A, Ljm Mortelmans Luc JM. "Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches." Journal of Contingencies and Crisis Management 33, no. 2 (2025): -.
Harvard	H Farhat HASSAN et al. (2025) 'Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches', Journal of Contingencies and Crisis Management, 33(2), pp. -.
Vancouver	H Farhat HASSAN, Am Batt Alan MICHAEL, Mc Helou Mariana CHARBEL, H Shin HEEJUN, J Laughton JAMES, C Dumbeck CAROLYN, et al.. Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches. Journal of Contingencies and Crisis Management. 2025;33(2):-.
BibTex	@article{ author = {H Farhat HASSAN and Am Batt Alan MICHAEL and Mc Helou Mariana CHARBEL and H Shin HEEJUN and J Laughton JAMES and C Dumbeck CAROLYN and A Dehghani AREZOO and F Rezaei FATEMEH and Na Bajow Nidaa A and Ljm Mortelmans Luc JM}, title = {Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches}, journal = {Journal of Contingencies and Crisis Management}, volume = {33}, number = {2}, pages = {-}, year = {2025} }
RIS	TY - JOUR AU - H Farhat HASSAN AU - Am Batt Alan MICHAEL AU - Mc Helou Mariana CHARBEL AU - H Shin HEEJUN AU - J Laughton JAMES AU - C Dumbeck CAROLYN AU - A Dehghani AREZOO AU - F Rezaei FATEMEH AU - Na Bajow Nidaa A AU - Ljm Mortelmans Luc JM TI - Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches JO - Journal of Contingencies and Crisis Management VL - 33 IS - 2 SP - EP - PY - 2025 ER -

Style

Citing Format

MLA

H Farhat HASSAN, et al.. "Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches." Journal of Contingencies and Crisis Management, vol. 33, no. 2, 2025, pp. -.

APA

H Farhat HASSAN, Am Batt Alan MICHAEL, Mc Helou Mariana CHARBEL, H Shin HEEJUN, J Laughton JAMES, C Dumbeck CAROLYN, A Dehghani AREZOO, F Rezaei FATEMEH, Na Bajow Nidaa A, Ljm Mortelmans Luc JM (2025). Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches. Journal of Contingencies and Crisis Management, 33(2), -.

Chicago

H Farhat HASSAN, Am Batt Alan MICHAEL, Mc Helou Mariana CHARBEL, H Shin HEEJUN, J Laughton JAMES, C Dumbeck CAROLYN, A Dehghani AREZOO, F Rezaei FATEMEH, Na Bajow Nidaa A, Ljm Mortelmans Luc JM. "Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches." Journal of Contingencies and Crisis Management 33, no. 2 (2025): -.

Harvard

H Farhat HASSAN et al. (2025) 'Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches', Journal of Contingencies and Crisis Management, 33(2), pp. -.

Vancouver

H Farhat HASSAN, Am Batt Alan MICHAEL, Mc Helou Mariana CHARBEL, H Shin HEEJUN, J Laughton JAMES, C Dumbeck CAROLYN, et al.. Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches. Journal of Contingencies and Crisis Management. 2025;33(2):-.

BibTex

@article{ author = {H Farhat HASSAN and Am Batt Alan MICHAEL and Mc Helou Mariana CHARBEL and H Shin HEEJUN and J Laughton JAMES and C Dumbeck CAROLYN and A Dehghani AREZOO and F Rezaei FATEMEH and Na Bajow Nidaa A and Ljm Mortelmans Luc JM}, title = {Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches}, journal = {Journal of Contingencies and Crisis Management}, volume = {33}, number = {2}, pages = {-}, year = {2025} }

RIS

TY - JOUR
AU - H Farhat HASSAN
AU - Am Batt Alan MICHAEL
AU - Mc Helou Mariana CHARBEL
AU - H Shin HEEJUN
AU - J Laughton JAMES
AU - C Dumbeck CAROLYN
AU - A Dehghani AREZOO
AU - F Rezaei FATEMEH
AU - Na Bajow Nidaa A
AU - Ljm Mortelmans Luc JM
TI - Evaluating the Performance of Agreement Metrics in a Delphi Study on Chemical, Biological, Radiological and Nuclear Major Incidents Preparedness Using Classical and Machine Learning Approaches
JO - Journal of Contingencies and Crisis Management
VL - 33
IS - 2
SP -
EP -
PY - 2025
ER -

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Abstract