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.
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http://dx.doi.org/10.1186/s12912-024-02486-8 | DOI Listing |
PeerJ
January 2025
Department of Public Health, Faculty of Medicine, Izmir Democracy University Buca Seyfi Demirsoy Training and Research Hospital, İzmir, Turkey.
Background: This study was conducted to assess how students' disaster literacy was affected by the Disaster Medicine Clinical Training Program at the Izmir Democracy University Faculty of Medicine (IDUFM) during the academic year 2022-2023.
Methods: Using an experimental method involving experimental and control groups, measurements were made at different times. The sample consisted of 5th-year students at IDUFM for the experimental group, while the control group was composed of 3rd- and 4th-year students from different buildings with limited interaction with the experimental group.
BMC Nurs
January 2025
Ege University, Medicine Faculty, Emergency Medicine Department, Izmir, Turkey.
Background: Disaster nursing involves systematic and professional care provided to communities affected by natural or man-made disasters. With limited resources in global disaster settings, nurses play a crucial role in disaster management. The aim of this study is to investigate the impact of integrating 'Disaster Nursing' into nursing curricula on nursing students' perceptions of disaster literacy and preparedness.
View Article and Find Full Text PDFDisaster Med Public Health Prep
January 2025
Burdur Mehmet Akif Ersoy University, Faculty of Health Sciences, Department of Emergency Aid and Disaster Management, Burdur, Turkey.
Objectives: This methodological study aimed to adapt the DLS, introduced for individuals aged 18-60 years, to those aged 60 years and older and to determine its psychometric properties.
Methods: We collected the data between December 15, 2021 and April 18, 2022. We carried out the study with a sample of 60 years and older living in the city center of Burdur, Turkey.
Sci Rep
December 2024
School of Nursing, Johns Hopkins University, Baltimore, MD, USA.
Introduction: Emerging and re-emerging infectious diseases continue to pose a severe threat to public health in Sub-Saharan Africa (SSA) and globally. Community-related interventions, such as community e-Health literacy, can contribute to the preparedness to respond effectively to emerging and re-emerging infectious diseases. This study investigated the relationship between e-Health literacy and SSA countries' perceptions of the importance of readiness for potential pandemics.
View Article and Find Full Text PDFBioData Min
December 2024
Master of Public Health Program, Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Introduction: The transformative feature of Artificial Intelligence (AI) is the massive capacity for interpreting and transforming unstructured data into a coherent and meaningful context. In general, the potential that AI will alter traditional approaches to student research and its evaluation appears to be significant. With regard to research in global health, it is important for students and research experts to assess strengths and limitations of GenAI within this space.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!