Volume 23, Issue 2 (6-2020)
jha 2020, 23(2): 76-88 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
saffari darberazi A, malekinejad P, Ziaeian M, Ajdari A. Designing a comprehensive model of hospital resilience in the face of COVID-19 disease. jha 2020; 23 (2) :76-88
URL: http://jha.iums.ac.ir/article-1-3312-en.html
URL: http://jha.iums.ac.ir/article-1-3312-en.html
1- PhD,Head of Research and Technology, Yazd University of Applied Science and Technology (UAST), Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran , asafaari@gmail.com
2- PhD Student ,Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran.
3- Master, Faculty of Management, Economics and Accounting, Shahid Ashrafi University of Isfahan, Isfahan, Iran.
4- PhD, Faculty of Management, Accounting and Economics, Islamic Azad University of Yazd, Yazd, Iran.
2- PhD Student ,Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran.
3- Master, Faculty of Management, Economics and Accounting, Shahid Ashrafi University of Isfahan, Isfahan, Iran.
4- PhD, Faculty of Management, Accounting and Economics, Islamic Azad University of Yazd, Yazd, Iran.
Abstract: (5379 Views)
Introduction: Introduction: Health centers must can adapt quickly to catastrophic events, such as natural and human disasters. One way to face various disasters in health centers is to increase resilience. This study tries to identify the affecting factors on hospital resilience and the relationship between them, to design a comprehensive model of hospital resilience in the face of COVID-19 disease.
Methods: First, the affecting factors on hospital resilience were identified using a research background study. Then, through an interpretive structural modeling (ISM) technique, a relationship model among the identified factors was obtained. The conceptual model obtained from ISM was goodness of fit by using the Smart PLS3 software. For this purpose, a questionnaire containing 33 questions was administered to 80 managers, experts, and staff of Yazd training hospitals.
Results: The results identified 8 general affecting factors on hospital resilience. The eight factors identified in this study were structured at 4 general levels by ISM. The initial level of the model consisted of "stability" and "communication system and information technology" factors. Also, the results of model goodness of fit confirmed the relationships formed by ISM.
Conclusion: The results of this study can be used by health managers for the country's hospitals resilience in the face of natural disasters and unforeseen accidents.
Methods: First, the affecting factors on hospital resilience were identified using a research background study. Then, through an interpretive structural modeling (ISM) technique, a relationship model among the identified factors was obtained. The conceptual model obtained from ISM was goodness of fit by using the Smart PLS3 software. For this purpose, a questionnaire containing 33 questions was administered to 80 managers, experts, and staff of Yazd training hospitals.
Results: The results identified 8 general affecting factors on hospital resilience. The eight factors identified in this study were structured at 4 general levels by ISM. The initial level of the model consisted of "stability" and "communication system and information technology" factors. Also, the results of model goodness of fit confirmed the relationships formed by ISM.
Conclusion: The results of this study can be used by health managers for the country's hospitals resilience in the face of natural disasters and unforeseen accidents.
Type of Study: Review |
Subject:
Health Services Management
Received: 2020/07/25 | Accepted: 2020/06/30 | Published: 2020/06/30
Received: 2020/07/25 | Accepted: 2020/06/30 | Published: 2020/06/30
References
1. Albanese J, Birnbaum M, Cannon C, Cappiello J, Chapman E, Paturas J, et al. Fostering disaster resilient communities across the globe through the incorporation of safe and resilient hospitals for community-integrated disaster responses. Prehosp Disaster Med. 2008;23(5):385-90. [DOI:10.1017/S1049023X00006105]
2. Labarda C, Labarda MDP, Lamberte EE. Hospital resilience in the aftermath of Typhoon Haiyan in the Philippines. Disaster Prev Manag. 2017;26(4):424-436. [DOI:10.1108/DPM-02-2017-0025]
3. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708-20. [DOI:10.1056/NEJMoa2002032]
4. Lin J, Ren Y, Gan H, Chen Y, Huang Y, You X. Factors Influencing Resilience of Medical Workers from Other Provinces to Wuhan Fighting Against 2019 Novel Coronavirus Pneumonia. BMC Psychiatry. 2020; [DOI:10.21203/rs.3.rs-17931/v1]
5. Foureur M, Besley K, Burton G, Yu N, Crisp J. Enhancing the resilience of nurses and midwives: Pilot of a mindfulnessbased program for increased health, sense of coherence and decreased depression, anxiety and stress. Contemp Nurse. 2013;45(1):114-25. [DOI:10.5172/conu.2013.45.1.114]
6. Aiello A, Young‐Eun Khayeri M, Raja S, Peladeau N, Romano D, Leszcz M, et al. Resilience training for hospital workers in anticipation of an influenza pandemic. J Contin Educ Health Prof. 2011;31(1):15-20. [DOI:10.1002/chp.20096]
7. Russo C, Calo O, Harrison G, Mahoney K, Zavotsky KE. Resilience and coping after hospital mergers. Clin nurse Spec. 2018;32(2):97-102. [DOI:10.1097/NUR.0000000000000358]
8. Pishnamazzadeh M, Sepehri MM, Ostadi B. An Assessment Model for Hospital Resilience according to the Simultaneous Consideration of Key Performance Indicators: A System Dynamics Approach. Perioper Care Oper Room Manag. 2020;20:100118. [DOI:10.1016/j.pcorm.2020.100118]
9. Olu O. Resilient health system as conceptual framework for strengthening public health disaster risk management: An african viewpoint. Front public Heal. 2017;5:263. [DOI:10.3389/fpubh.2017.00263]
10. Zhong S, Clark M, Hou X-Y, Zang Y-L, Fitzgerald G. Development of hospital disaster resilience: conceptual framework and potential measurement. Emerg Med J. 2014;31(11):930-8. [DOI:10.1136/emermed-2012-202282]
11. Cimellaro GP, Malavisi M, Mahin S. Factor analysis to evaluate hospital resilience. ASCE-ASME J Risk Uncertain Eng Syst Part A Civ Eng. 2018;4(1):4018002. [DOI:10.1061/AJRUA6.0000952]
12. Jolgehnejad AK, Kahnali RA, Heyrani Al. Factors Influencing Hospital Resilience. Disaster Med Public Health Prep. 2020;1-8. [DOI:10.1017/dmp.2020.112]
13. Hosseini Bamkan S M, Malekinejad P, Ziaeian M. Investigation and analysis of urban service supply chain (Case study: Isfahan Municipality). Urban And Rural Management. 2019;18(56):73-92. [In Persian].
14. Warfield JN. Developing interconnection matrices in structural modeling. IEEE Trans Syst Man Cybern. 1974;4(1):81-7. [DOI:10.1109/TSMC.1974.5408524]
15. Talib F, Rahman Z, Qureshi MN. Analysis of interaction among the barriers to total quality management implementation using interpretive structural modeling approach. Benchmarking. 2011;18(4):563-587. [DOI:10.1108/14635771111147641]
16. Barclay D, Higgins C, Thompson R. The partial least squares (PLS) approach to casual modeling: personal computer adoption ans use as an Illustration. 1995;2(2):285-309.
17. Fallah-Aliabadi S, Ostadtaghizadeh A, Ardalan A, Fatemi F, Khazai B, Mirjalili MR. Towards developing a model for the evaluation of hospital disaster resilience: a systematic review. BMC Health Serv Res. 2020;20(1):64. [DOI:10.1186/s12913-020-4915-2]
18. Reggiani A, Nijkamp P, Lanzi D. Transport resilience and vulnerability: The role of connectivity. Transp Res part A policy Pract. 2015;81:4-15. [DOI:10.1016/j.tra.2014.12.012]
19. Longstaff PH, Yang S-U. Communication management and trust: their role in building resilience to "surprises" such as natural disasters, pandemic flu, and terrorism. Ecol Soc. 2008;13(1):1-15. [DOI:10.5751/ES-02232-130103]
20. Paton D, Smith L, Violanti J. Disaster response: risk, vulnerability and resilience. Disaster Prev Manag. 2000;9(3):173-180. [DOI:10.1108/09653560010335068]
21. McEntire DA. Disaster response and recovery: strategies and tactics for resilience.2nd. Miami: John Wiley & Sons; 2015.
22. Luthar SS, Cicchetti D, Becker B. Research on resilience: Response to commentaries. Child Dev. 2000;71(3):573-5. [DOI:10.1111/1467-8624.00168]
23. Marshall A, Alqahtani S, Ridgway A, Walter C, Gamble R, Mailler R. Combining coordination with usage policies to improve mission scheduling resilience. Resilience Week (RWS). 2015 Aug 18-20; Philadelphia: IEEE; 2015. p. 1-6. [DOI:10.1109/RWEEK.2015.7287425]
24. Li Q, Dong S, Mostafavi A. Modeling of inter-organizational coordination dynamics in resilience planning of infrastructure systems: A multilayer network simulation framework. PLoS One. 2019;14(11):e0224522. [DOI:10.1371/journal.pone.0224522]
25. Chen C-C, Tsai Y-H, Schonfeld P. Schedule coordination, delay propagation, and disruption resilience in intermodal logistics networks. Transp Res Rec. 2016;2548(1):16-23. [DOI:10.3141/2548-03]
26. Burns MG. Logistics and transportation security: a strategic, tactical, and operational guide to resilience. Florida: CRC Press; 2015. [DOI:10.1201/b19414]
27. VanVactor JD. Cognizant healthcare logistics management: ensuring resilience during crisis. Int J Disaster Resil Built Environ. 2011; 2(3):245-255. [DOI:10.1108/17595901111167114]
28. Robertson IT, Cooper CL, Sarkar M, Curran T. Resilience training in the workplace from 2003 to 2014: A systematic review. J Occup Organ Psychol. 2015;88(3):533-62. [DOI:10.1111/joop.12120]
29. Davari A, Rezazadeh A. Structural equation modeling with PLS software. 3rd ed. Tehran: University Jihad Publications; 2015. [In Persian]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Site Keywords
Health, Administration, Scientific Article, Iran University of Medical Sciences,
