Objective: The purpose of this study was to examine the prevalence of disaster preparedness and to explore associated factors among emergency nurses in Guangdong Province, China. Methods: In this descriptive, cross-sectional study, the mainland China version of the Disaster Preparedness Evaluation Tool was used to collect data from 633 nurses working in 26 emergency departments, in August 2018. Descriptive analyses were used to examine the disaster preparedness, and multiple linear regression analysis was used to investigate associated factors. Results: The perceived disaster preparedness of emergency nurses was at a moderate level. Among the 5 dimensions, the score for disaster management was lowest. Emergency nurses’ disaster training (r = .26; P < 0.001) and drill experiences (r = .22; P < 0.001) were significantly correlated with disaster preparedness. Six significant factors associated with disaster preparedness were identified in this study: age, gender, disaster training experience, disaster drill experience, willingness, and educational level (R2 = .14; F = 18.20; P < 0.001). Conclusions: Hospitals and nurse managers should carry out interdisciplinary and multidisciplinary cooperation to improve emergency nurses’ disaster preparedness, especially disaster management. Organizing disaster simulation exercises, providing psychological support and safety considerations, and formulating disaster nursing training programs may be beneficial for emergency nurses’ disaster preparedness.

Background: Emergency nurses play a major role in disaster relief in mainland China, but there is no valid instrument to measure the extent of their disaster preparedness. The Disaster Preparedness Evaluation Tool© is a reliable instrument to assess the disaster preparedness of nurse practitioners. The tool has been translated and validated in Saudi Arabia, Taiwan, China and the United States of America. Objectives: This study aimed at translating and adapting the Disaster Preparedness Evaluation Tool© (DPET) for emergency nurses in mainland China and determining its psychometric properties. Design, Settings and Participants: A total of 2 cross-sectional online surveys were conducted in the emergency departments of 26 public grade III-A hospitals in Guangdong, mainland China. In the first study, 633 emergency nurses were recruited from May to August, 2018. In the second study, 205 were recruited in April 2019. Methods: The instrument was adapted through rigorous forward-backward translation, face validity, and pre-test processes. Exploratory factor and parallel analyses were used in the first study. Confirmatory factor analysis, internal consistency and split-half reliability were used in the second study. Results: Exploratory factor and parallel analyses extracted a 5-factor solution comprising of 34 items that accounted for 64.06% of the total variance. The fit indices indicated a good model fit. The reliability was good, as indicated by a Cronbach’s alpha of 0.97 and a split-half reliability coefficient of 0.97. Conclusion: The mainland China version of the DPET (DPET-MC) was a reliable and valid instrument and can be used in practice.

Stroke patients suffer from public stigma because strokes cause visible disability and heavy social burden. However, existing tools measuring stroke-related stigma do not consider public stigma. The aim of this study was to develop and evaluate a public stigma of stroke scale (PSSS). This cross-sectional study recruited 730 participants, aged above 18 years, with no diagnosis of stroke before. Scale items were generated after reviewing relevant literature and conducting interviews. An expert panel evaluated the validity and reliability of a preliminary scale. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), bifactor CFA (B-CFA), Exploratory structural equation modelling (ESEM), bifactor-ESEM (B-ESEM) were performed to extract factors and evaluate fit on the factor structures. The Omega coefficient was 0.93, and the test–retest reliability coefficient was 0.721. The EFA extracted four factors: inherent ideology, aesthetic feelings, avoidance behaviour, and policy attitudes. These explained 61.57% of the total variance in the data. The four-factor model was confirmed by B-CFA, and met the fitness criteria. The PSSS yields satisfactory psychometric properties and can be used to assess stroke-related public stigma.