Characteristics of Resilience Among Disaster Rescue Workers: A Systematic Review

Mao, Xiaorong; Fung, Olivia Wm; Hu, Xiuying; Loke, Alice Yuen

doi:10.1017/dmp.2020.192

Cited by 14 publications

(10 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The resulting four-factor model supported the findings of our qualitative interview study on the views of disaster among healthcare rescuers (Mao et al 2019), and of a review of the literature on the characteristics of resilience (Mao, Fung, et al 2020). However, four domains-optimism, preparations for disaster, perceived control, and coping strategies-that emerged in our modified Delphi study were not supported in this study and were removed accordingly.…”

Section: Discussionsupporting

confidence: 72%

“…The aim of this study was to evaluate the psychometric properties of a disaster resilience measuring tool (DRMT-C) developed by the research team based on a series of studies (Mao et al 2019;Mao, Fung, et al 2020;Mao, Wang, et al 2020), and according to the guidelines on the development of scales (DeVellis 2017). The ultimate aim of establishing the psychometric properties of the measuring instrument was to measure disaster resilience in healthcare rescuers.…”

Section: Discussionmentioning

confidence: 99%

“…These resilience tools do not consist of the two domains of disaster resilience identified in this study: positive growth and altruism. These two domains of disaster resilience were important in disaster healthcare rescuers, playing essential roles in ''bouncing back,'' and unselfish service to others, while adapting to adverse events (Mao et al 2019;Mao, Fung, et al 2020). These two unique domains in the concept of disaster resilience highlighted the importance of healthcare rescuer resilience during and after deployment.…”

Section: Discussionmentioning

confidence: 99%

“…To address this need, the research team developed a prototype tool for measuring the disaster resilience of healthcare rescue workers by conducting a series of studies. The process began with an extensive review of the literature on the characteristics of resilience among disaster rescue workers (Mao, Fung, et al 2020), and a focus group interview study on the views of healthcare rescuers who have been deployed to disaster rescue work on disaster resilience (Mao et al 2019). A scoping review of resilience scales for adults was conducted, and the results of the two previous studies were incorporated, to develop a prototype disaster resilience tool for healthcare rescuers (Mao, Wang, et al 2020).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Mao

Chen

et al. 2021

Int J Disaster Risk Sci

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mao

Chen

et al. 2021

Int J Disaster Risk Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, resilience has been regarded as a key factor in reducing disaster risk; it is the individual's ability to survive accidents and the ability to resist disasters, recover and reorganize to deal with disasters. 16,18,19 The disaster management principles comprise 4 phases: prevention, preparedness, response, reconstruction, and recovery (PPRR). The World Health Organization (WHO) divides the entire disaster process into 3 stages: pre-disaster (disaster prevention, disaster preparedness), during a disaster (response during a disaster), and after a disaster (reconstruction and recovery).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Resilience, Coping Style, Anxiety, and Depression Among Rescue Nurses on EMTs During the Disaster Preparedness Stage in Sichuan, China: A Descriptive Cross-Sectional Survey

Huang

et al. 2022

Disaster med. public health prep.

View full text Add to dashboard Cite

Objective: The purpose of this study was to investigate the current situation and influencing factors of resilience, coping style, anxiety, and depression among rescue nurses on Emergency Medical Teams (EMTs) during the disaster preparedness stage, and to explore the relationship between resilience, coping style, anxiety, and depression. Methods: A descriptive cross-sectional survey was used. The convenience sampling method was used to select rescue nurses from 4 national EMTs in Sichuan Province, China. CD-RISC, SCSQ, Zung SAS, and SDS were used as research tools. Results: A total of 197 valid questionnaires were returned, with a 98.5% response rate. The results of multiple regression analysis showed that average monthly income, depression level, and coping style were the main factors affecting nurses’ resilience. Resilience was significantly and positively correlated with coping style and negatively correlated with anxiety and depression. There were significant differences in SCSQ, Zung SAS, and SDS scores among Clusters. Conclusion: In the disaster preparedness stage, rescue nurses on EMTs have a medium level of resilience and good coping styles, but their levels of anxiety and depression are high. It is suggested that more attention should be devoted to the target population to formulate effective intervention plans, reduce psychological pressures, and improve their capacity for coping with disasters.

show abstract

Correlating Personality Traits With Acute Stress Responses in Earthquake Simulations: An HRV and RESP Analysis

Li,

Zhu,

Guan

et al. 2024

Stress and Health

View full text Add to dashboard Cite

Earthquakes, as significant natural disasters, still cannot be accurately predicted today. Although current earthquake early warning systems can provide alerts several seconds in advance, acute stress responses (ASR) in emergency situations can waste these precious escape seconds. To investigate the correlation between personality and ASR, this study collected the temperament and character of all participants using the Chen Huichang‐60 Temperament Scale and the DISC Personality Inventory. In addition, this study simulated growing earthquakes in an earthquake experience hall, collecting heart rate variability and respiration signal variations throughout the process from subjects. Multivariate analysis of variance (MANOVA) and Toeplitz Inverse Covariance‐Based Clustering methods were used to analyse the differences and connections between them. Furthermore, this study employed a deep learning model that combines Convolutional Neural Networks (CNN) and Long Short‐Term Memory (LSTM) to predict ASR across personalities. This model used datasets from the majority dataset of a certain personality and a single participant, respectively, and showed different performance. The results are as follows. After categorising participants based on personality test results, MANOVA revealed significant differences between the personality groups Influence‐Choleric and Influence‐Sanguine (p = 0.001), Influence‐Phlegmatic and Steadiness‐Sanguine (p = 0.023), Influence‐Sanguine and Steadiness‐Sanguine (p < 0.001) and Influence‐Sanguine and Steadiness‐Phlegmatic (p < 0.001), as well as across different earthquake stages (p < 0.01). The clustering method quantified stress responses over time for different personalities and labelled ASR levels for use in supervised learning. Ultimately, the CNN‐LSTM model performed predictions of ASR using both personality and individual datasets, achieving the AUC of 0.795 and 0.72, demonstrating better prediction and classification effectiveness with the former. This study provides a new personality‐based method for earthquake stress management, creating possibilities for longitudinal stress research and prediction. It aids the general public in comprehending their own acute stress and allows authorities and communities to make practical, efficient disaster evacuation plans based on the overall situation of public ASR.

show abstract

Characteristics of Resilience Among Disaster Rescue Workers: A Systematic Review

Cited by 14 publications

References 64 publications

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

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

Analysis of Resilience, Coping Style, Anxiety, and Depression Among Rescue Nurses on EMTs During the Disaster Preparedness Stage in Sichuan, China: A Descriptive Cross-Sectional Survey

Correlating Personality Traits With Acute Stress Responses in Earthquake Simulations: An HRV and RESP Analysis

Contact Info

Product

Resources

About