Choi M, et al. Healthc Inform Res. 2012 Sep;18(3):191-198. http://dx.doi.org/10.4258/hir.2012.18.3.191

Fear of falling has been reported in a high percentage of community-dwelling elderly who both do and don't have a history of falling. The aims of this review are to: (a) elucidate the definition of fear of falling; (b) clarify measurements of fear of falling based on its definition; and (c) describe the risk factors for fear of falling. Despite the importance of the percentage and the consequences of fear of falling, its definition is still vague and warrants clarification. Based on a literature review, major fear of falling measurements involve the evaluation of fear of falling and use of a fall efficacy scale. Using a correct definition of fear of falling, nurses working close with older adults need to identify the different definitions of fear of falling and fall efficacy scale. In addition, nurses who work closely with older adults should encourage them to increase or maintain modifiable factors by maximizing their basic health status and enhancing their physical activity to decrease fear of falling.

As the use of artificial intelligence (AI) technologies, particularly generative AI (Gen AI), becomes increasingly prevalent in nursing education, it is paramount to address the ethical implications of their implementation. This article explores the realm of cyberethics (a field of applied ethics that focuses on the ethical, legal, and social implications of cybertechnology), highlighting the ethical principles of autonomy, nonmaleficence, beneficence, justice, and explicability as a roadmap for facilitating AI integration into nursing education. Research findings suggest that ethical dilemmas that challenge these five principles can emerge within the context of nursing education; however, adherence to these very principles, which is essential to improving patient care, can offer solutions to these dilemmas. To ensure the ethical and responsible use of Gen AI in nursing education, these principles must be woven into the fabric of curricula, and appropriate guidelines must be developed. Nurse educators have a pivotal role in strategizing comprehensive approaches for ethical AI integration, establishing clear guidelines, and instilling critical thinking among students. Fostering lifelong learning and adaptability is key to ensuring that future nurses can successfully navigate the constantly evolving landscape of health care technology. Future research should investigate the long-term impacts of AI utilization on learning outcomes and ethical decision-making.

This study examines the effectiveness of interventions for decreasing the fear of falling by synthesizing the results of individual studies of fear of falling prevention. The mean weighted effect sizes (MWES) of six studies are computed. The overall MWES is .21 (N = 957; 95% CI .068—.362). Results indicate that interventions are effective in reducing the fear of falling or increasing fall efficacy. The MWES is .249, .024, and .418 for the combined intervention, exercise intervention only, and hip protector groups, respectively. The MWES of the 3 months or less group was .000, and the 4-months group was .240. The MWES is .022, .228, and .418 for the facility-based, community-based, and home-based intervention groups, respectively. Overall, interventions were effective in decreasing fear of falling and increasing fall efficacy. Results suggest that the best outcome is obtained after 4 months, indicating that interventions need some time to obtain their effects.

Background: Given the wide range of metaverse technologies, there is a need to synthesize evidence of metaverse pedagogy used effectively for nursing education. Purpose: This umbrella review synthesized systematic reviews on the use of metaverse in nursing education. Methods: A search was performed in MEDLINE, EMBASE, CINAHL, Web of Science, and Education Full Text. This umbrella review was conducted with reference to the Joanna Briggs Institute (JBI) Reviewer's Manual and reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The JBI Critical Appraisal Checklist for Systematic Review was used to assess the quality of studies. Results: The final review comprised 15 articles published between 2013 and 2021, most of which indicate that metaverse interventions support increased knowledge, self-confidence, engagement, satisfaction, and performance in nursing students. Several articles in this review presented mixed findings related to certain learning outcomes. Conclusion: This umbrella review supports the viability and effectiveness of metaverse in nursing education.