The associated factors of disaster literacy among nurses in China: a structure equation modelling study
Year: 2024
Type: article
Study design: Observational Study
Abstract: AIMS: To examine the association of disaster literacy with collectivism, social support, psychological resilience and self-efficacy among nurses and analyze the influencing mechanisms among these factors. BACKGROUND: Nurses' disaster literacy is important for future preparation. However, there is a paucity of research in this field. METHODS: From January to August 2023, we recruited 1573 nurses in 15 hospitals in Zhejiang province, China using convenient sampling. Based on Social-Ecological Model, questionnaires regarding collectivism, social support, psychological resilience, self-efficacy and disaster literacy were distributed via online platform. Data were analyzed using structural equation model to examine the relationships between the study variables. RESULTS: Nurses had a medium level of disaster literacy with the lowest score in critical literacy. Nurses' collectivism not only had positive direct effect on disaster literacy, but also had indirect pathways from social support, psychological resilience and self-efficacy to influence the level of disaster literacy. CONCLUSIONS: Multilevel factors including collectivism, social support, psychological resilience and self-efficacy were associated with disaster literacy. Understanding the influencing mechanism would inform effective interventions. IMPLICATIONS FOR NURSING MANAGEMENT: Our findings illustrate the importance for nurse managers, administrators and authorities to work together to develop and implement effective nursing curriculum and training programs to improve nurses' disaster literacy for future preparation.
Source: BMC Nursing
Authors: Yan Yang, Yuping Zhang, Yuanfei Liu, Qian Li, Lizhu Wang, Yong Du, Meijuan Lan, Jingfen Jin
Language: English
Subfield: Emergency Medical Services
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,890
OpenAlex ID: W4404695359
