Volume 12 Issue 3
Dec.  2021
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Xiaorong Mao, Kang Chen, Xiuying Hu, Xianxiu Wen, Alice Yuen Loke. Establishment of the Psychometric Properties of a Disaster Resilience Measuring Tool for Healthcare Rescuers in China: A Cross-Sectional Study[J]. International Journal of Disaster Risk Science, 2021, 12(3): 381-393. doi: 10.1007/s13753-021-00342-w
Citation: Xiaorong Mao, Kang Chen, Xiuying Hu, Xianxiu Wen, Alice Yuen Loke. Establishment of the Psychometric Properties of a Disaster Resilience Measuring Tool for Healthcare Rescuers in China: A Cross-Sectional Study[J]. International Journal of Disaster Risk Science, 2021, 12(3): 381-393. doi: 10.1007/s13753-021-00342-w

Establishment of the Psychometric Properties of a Disaster Resilience Measuring Tool for Healthcare Rescuers in China: A Cross-Sectional Study

doi: 10.1007/s13753-021-00342-w
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We are grateful to the Director of the Office of Emergency Response in Sichuan Province who assisted in the process of collecting data for this research. Also, we would like to express our gratitude to all the personnel who participated in this study. Finally, we deeply thank those who helped us forward and back translate the DRMT into Chinese and English, and the experts who reviewed the translated version of the DRMT before data collection for this study.

  • Available Online: 2021-12-25
  • Publish Date: 2021-12-25
  • The aim of this study was to test the validity and reliability of a tool for measuring the disaster resilience of healthcare disaster rescuers. A cross-sectional study involving 936 healthcare disaster rescuers of the Sichuan Disaster Response Team was conducted to establish the psychometric properties of the disaster resilience measuring tool (DRMT). Item analysis, exploratory factor analysis, confirmatory factor analysis, and correlation analysis were adopted to analyze the data. Item analysis showed that all but three items had the critical ratio over 3, which indicates adequate discriminability for inclusion in the measuring tool. The exploratory factor analysis showed that 65.93% of the total variance was explained by four factors—self-efficacy, social support, positive growth, and altruism. The confirmatory factor analysis showed goodness of fit for the four-factor model: CMIN/DF (2.846), GFI (0.916 ≥ 0.90), CFI (0.949 ≥ 0.90), AGFI (0.891 ≥ 0.80), and RMSEA (0.063 ≤ 0.08). Criterion validity demonstrated significant associations of the DRMT and the Connor-Davidson Resilience Scale (P < 0.01, r=0.566). Convergent validity was established by correlation with stress (P < 0.05, r=-0.095), depression (P < 0.01, r=-0.127), posttraumatic stress disorder-PCL-C (P < 0.05, r=-0.100), compassion satisfaction (P < 0.01, r=0.536), and burnout (P < 0.01, r=-0.330). The DRMT demonstrated adequate internal consistency (Cronbach’s alpha > 0.84) and stability over the two-week study period (intraclass correlation coefficient > 0.85), and a cut-off point of 61 was suggested. The disaster resilience measuring tool has satisfactory psychometric properties and is a valid, reliable, and valuable instrument for assessing disaster resilience in healthcare rescue workers. The scale needs to be tested further among other populations and those from other cultures.
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