Nurse Education in Practice最新文献

英文中文

Critical reflections from a transdisciplinary team on deploying an AI-enabled robot in long-term care 一个跨学科团队对在长期护理中部署人工智能机器人的批判性思考

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2026-01-06 DOI: 10.1016/j.nepr.2026.104709

Julia Banco, Albin Soni, Karen Lok Yi Wong, Lily Haopu Ren, Rachel Xia, Shambhavi Arora, Lillian Hung

Aim

This study presents our reflections as student researchers working within a transdisciplinary, intergenerational team that implemented Aether, an AI-enabled care robot, in a long-term care (LTC) facility for older adults. We examine our roles and contributions to the project’s transdisciplinary and intergenerational research process.

Background

Transdisciplinary and intergenerational collaboration have been proven beneficial for implementing AI-driven technologies in LTC. Few studies have explored students’ roles in transdisciplinary or intergenerational collaboration and their opportunities for mutual learning experiences and ethically grounded innovations.

Design

This critical reflection paper is part of a larger study examining the deployment of Aether. Our research design focuses on capturing our experiences as a team of student researchers through reflective inquiry, emphasizing our role in the implementation and deployment process.

Methods

Reflections were gathered through individual and group sessions using Rolfe’s (2001) Critical Reflection Framework. Reflexive thematic analysis, combining inductive and deductive coding, was used to identify key themes from our experiences.

Results

Three reflexive themes emerged: (1) The transformative role of transdisciplinary collaboration in addressing real-world challenges; (2) The importance of intergenerational collaboration with industry; and (3) The value of field-based experience in bridging theory and practice.

Conclusions

This study proposes a framework of practical tips, “SHINE,” to support future technology implementations in LTC through intergenerational and transdisciplinary collaboration. This framework highlights the value of student engagement in hands-on, collaborative research settings and encourages future research to focus on the critical role of student engagement in complex care environments.

本研究提出了我们作为学生研究人员在一个跨学科、跨代团队中工作的思考，该团队在老年人长期护理（LTC）设施中实施了支持人工智能的护理机器人以太。我们审视我们在项目的跨学科和代际研究过程中的角色和贡献。跨学科和代际协作已被证明有利于在LTC中实施人工智能驱动技术。很少有研究探讨学生在跨学科或代际合作中的角色，以及他们相互学习经验和道德创新的机会。这篇重要的反思论文是研究以太部署的大型研究的一部分。我们的研究设计侧重于通过反思性调查来捕捉我们作为一个学生研究团队的经验，强调我们在实施和部署过程中的作用。方法采用罗尔夫（2001）批判性反思框架，通过个人和小组会议收集反思。反身性主题分析，结合归纳和演绎编码，从我们的经验中确定关键主题。三个反思性主题浮现出来：(1)跨学科合作在应对现实挑战中的变革作用；(2)与产业的代际合作的重要性；(3)实地经验在衔接理论与实践方面的价值。本研究提出了一个实用技巧框架“SHINE”，通过代际和跨学科合作，支持LTC中未来的技术实施。该框架强调了学生参与实践、合作研究环境的价值，并鼓励未来的研究关注学生参与在复杂护理环境中的关键作用。

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引用次数: 0

Feasibility and acceptability of immersive virtual reality for end-of-life care education among nurses in high-context cultures 沉浸式虚拟现实在高情境文化中护士临终关怀教育的可行性和可接受性。

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2026-01-06 DOI: 10.1016/j.nepr.2026.104712

Cheng-Pei Lin , Lu-Yen Anny Chen , Yu-Chi Chen , Jou-Chun Lin , Ding-Han Wang , Heng-Hsin Tung

Aim

to explore the feasibility and acceptability of implementing an immersive virtual reality (IVR) end-of-life care training program among nursing staff in a high-context culture.

Background

End-of-life nursing education is limited in high-context cultures where discussing death and dying is emotionally challenging. IVR offers an innovative possibility to enhance nurses’ knowledge and empathy and guide patients’ medical decision-making. However, the feasibility and acceptability of this approach in end-of-life care training for nursing staff remain underexplored.

Design

A mixed-methods pre- and post-intervention design supplemented with free-text qualitative feedback.

Methods

Nursing staff with at least 3 months of clinical experience participated in a IVR-based simulation. Pre- and post-intervention knowledge, learning experience, satisfaction and qualitative responses were collected. Data were analysed using descriptive statistics, paired t-tests and content analysis of open-ended responses.

Results

Fifty nurses were recruited (mostly 21–29 years old females with a college degree). Post-intervention knowledge score improved significantly (pre: M 9.18 SD 0.66; post: M 9.58 SD 0.58, Z = -3.780, p < .001). Overall, participants reported high satisfaction (mean: 27.26/30) with positive feedback on the usefulness of VR (M = 87.50/100), the appropriateness of the content in the module (M = 28.32/30) and minimal physical discomfort (M = 2.44/8). IVR was rated more favourably than traditional training (M = 90.60/100), but was viewed as a complementary tool.

Conclusion

IVR-based end-of-life care training is feasible and well-received by nursing staff in high-context cultures, enhancing knowledge and motivation. While promising, IVR should be integrated as a supplementary strategy alongside conventional training to preserve contextual depth in palliative care education.

目的：探讨在高情境文化中，护理人员实施沉浸式虚拟现实（IVR）临终关怀培训计划的可行性和可接受性。背景：临终护理教育在高语境文化中是有限的，在那里讨论死亡和临终是情感上的挑战。IVR提供了一种创新的可能性，可以提高护士的知识和同理心，指导患者的医疗决策。然而，这种方法在护理人员临终关怀培训中的可行性和可接受性仍未得到充分探讨。设计：干预前后混合方法设计，辅以自由文本定性反馈。方法：具有3个月以上临床经验的护理人员参与基于ivr的模拟。收集干预前和干预后的知识、学习经验、满意度和定性反应。数据分析采用描述性统计、配对t检验和开放式回答的内容分析。结果：共招募护士50人，年龄以21 ~ 29岁女性为主，大专以上学历。干预后知识得分显著提高（干预前：M 9.18 SD 0.66；干预后：M 9.58 SD 0.58, Z = -3.780,p ）结论：基于ivr的临终关怀培训在高情境文化下是可行的，并且受到护理人员的欢迎，提高了知识和积极性。IVR虽然很有前途，但应该与传统培训一起作为补充策略进行整合，以保持姑息治疗教育的情境深度。

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引用次数: 0

Analysis of responses to comprehensive nursing exam questions by large language models: A comparison of ChatGPT, DeepSeek and students 基于大语言模型的综合护理考试试题回答分析：ChatGPT、DeepSeek和学生的比较

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2026-01-04 DOI: 10.1016/j.nepr.2025.104691

Li Ma , Qiankun Liu , Pengyao Li , Juju Huang , Yang Xu , Jiaxue Pang , Hui Xie

Objective

This study aimed to evaluate the potential of large language models (LLMs) in nursing education and practice by comparing their performance with that of baccalaureate nursing graduates on a comprehensive nursing examination.

Background

The digital transformation of nursing education is underway and LLMs, with their advanced natural language processing capabilities, show significant potential in this field.

Design

This study employed a cross-sectional design.

Methods

This study compared the graduation exam performance of 221 Chinese nursing undergraduates with two LLMs (ChatGPT, DeepSeek), analyzing answer accuracy and scores via descriptive statistics and chi-square tests.

Results

On questions assessing higher-order thinking skills, students demonstrated superior clinical evaluation and application abilities compared with both ChatGPT and DeepSeek. Conversely, students' clinical analysis ability was significantly lower than that of both AI models (74.77 % vs. 88.24 % vs. 94.12 %, p < 0.05). No statistically significant difference was found in memory ability among the three groups (p > 0.05). For comprehension ability, DeepSeek's performance was superior to that of the students (81.82 % vs. 68.91 %).

Conclusions

This comparative study revealed that LLMs and nursing students demonstrated distinct patterns of strengths and limitations when answering comprehensive exam questions, indicating a potential complementarity. These findings provide preliminary evidence for future exploration of human-AI collaborative models in nursing education. Subsequent research should further investigate specific applications of LLMs in teaching and learning scenarios.

目的通过比较大语言模型（LLMs）与护理本科毕业生在综合护理考试中的表现，评估LLMs在护理教育和实践中的潜力。护理教育的数字化转型正在进行中，法学硕士以其先进的自然语言处理能力，在这一领域显示出巨大的潜力。本研究采用横断面设计。方法采用描述性统计和卡方检验的方法，对221名中国护理本科生（ChatGPT、DeepSeek）的毕业考试成绩进行比较。结果在评估高阶思维能力的问题上，学生的临床评估能力和应用能力均优于ChatGPT和DeepSeek。相反，学生的临床分析能力明显低于两种AI模型（74.77 % vs. 88.24 % vs. 94.12 %,p <； 0.05）。三组患者的记忆能力差异无统计学意义（p >； 0.05）。在理解能力方面，DeepSeek的表现优于学生（81.82 % vs. 68.91 %）。结论本比较研究显示，法学硕士和护理专业学生在回答综合试题时表现出明显的优势和局限性，具有潜在的互补性。这些发现为未来探索护理教育中人类-人工智能协同模式提供了初步依据。后续研究应进一步探讨法学硕士在教学和学习场景中的具体应用。

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引用次数: 0

From tools to transformation: Leveraging artificial intelligence (AI) and human-centered design to educate future nurse leaders 从工具到转型：利用人工智能（AI）和以人为本的设计来培养未来的护士领袖。

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2026-01-01 DOI: 10.1016/j.nepr.2025.104591

Miranda Hawks , Linda McCauley , Roy Simpson , Rosa I. Arriaga , Lalita Kaligotla

引用次数: 0

Emerging AI trends in nursing and midwifery education: A critical look at agentic AI and multimodal models 护理和助产教育中新兴的人工智能趋势：对代理人工智能和多模态模型的批判性观察。

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2026-01-01 DOI: 10.1016/j.nepr.2025.104646

Siobhan O’Connor , Jennie C. De Gagne , Vivian Hui

引用次数: 0

The importance of transparency, accountability, and verifiability when using artificial intelligence in nursing education 在护理教育中使用人工智能时，透明度、问责制和可验证性的重要性。

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2026-01-01 DOI: 10.1016/j.nepr.2025.104557

Sarah Oerther, Daniel B. Oerther

引用次数: 0

Equipping nurses for the reality of sexual violence in clinical practice 让护士在临床实践中了解性暴力的现实。

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2026-01-01 DOI: 10.1016/j.nepr.2025.104590

Chelsea L. Wedel

引用次数: 0

‘The Handover’ – Ireland’s first podcast for student nurses “交接”——爱尔兰首个面向护士学生的播客。

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2026-01-01 DOI: 10.1016/j.nepr.2025.104615

Erika Jones, Melissa Corbally

引用次数: 0

Cybersecurity education privacy in nursing disciplines – a bibliometric analysis 护理学科中的网络安全教育隐私——文献计量分析

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2025-12-30 DOI: 10.1016/j.nepr.2025.104685

Moti Zwilling , Naveh Eskinazi , Lani Ofri

Aim

As healthcare digitization accelerates, cybersecurity awareness is vital for nurses handling sensitive patient data. This study explores how cybersecurity education is represented in nursing literature.

Background

Nurses play a key role in protecting patient information, yet nursing education often lacks structured cybersecurity training. As digital threats grow, it is critical to examine academic coverage of this topic to inform future curricula.

Design

A bibliometric analysis was conducted to assess global research trends, key contributors and emerging topics related to cybersecurity awareness and education in nursing.

Methods

Using the Web of Science database (1993–2024), we analyzed 2498 publications with Python, Bibliometrix, Orange Tools and VOSviewer 1.6.20. We mapped citation networks, countries, institutions and authors and applied sentiment analysis, word clouds, topic modeling and t-SNE to visualize key themes.

Results

Publications on cybersecurity in nursing increased notably from 2015 to 2023, reflecting growing awareness. Common themes included data protection, privacy, HIPAA regulations and nurses’ perceptions of handling sensitive information. However, most nursing students still lack exposure to these topics in their studies. The analysis reveals a significant gap between academic discussion and formal curriculum integration.

Conclusions

Cybersecurity is underrepresented in nursing education despite increasing relevance. Greater collaboration between nursing educators and cybersecurity professionals is needed to embed critical topics—such as digital ethics, legal compliance and data privacy—into nursing programs, ensuring students are better equipped for digital healthcare environments.

随着医疗保健数字化的加速，网络安全意识对于处理敏感患者数据的护士至关重要。本研究探讨网路安全教育如何在护理文献中呈现。护士在保护患者信息方面发挥着关键作用，但护理教育往往缺乏结构化的网络安全培训。随着数字威胁的增长，检查这一主题的学术报道以告知未来的课程是至关重要的。进行了文献计量分析，以评估全球研究趋势、主要贡献者和与网络安全意识和护理教育相关的新兴主题。方法利用Web of Science数据库（1993-2024），使用Python、Bibliometrix、Orange Tools和VOSviewer 1.6.20对2498篇文献进行分析。我们映射了引文网络、国家、机构和作者，并应用情感分析、词云、主题建模和t-SNE来可视化关键主题。结果2015年至2023年，护理网络安全相关出版物数量显著增加，反映了人们对护理网络安全的认识不断提高。常见的主题包括数据保护、隐私、HIPAA法规和护士对处理敏感信息的看法。然而，大多数护理专业的学生在他们的学习中仍然缺乏这些主题的接触。分析表明，学术讨论与正式课程整合之间存在显著差距。结论网络安全在护理教育中的代表性不足，尽管相关性越来越高。护理教育工作者和网络安全专业人员之间需要加强合作，将数字伦理、法律合规和数据隐私等关键主题纳入护理课程，确保学生更好地适应数字医疗环境。

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引用次数: 0

Generative artificial intelligence for teaching and assessment in health professions education: A scoping review 生成性人工智能在卫生专业教育中的教学和评估：范围综述

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice

Pub Date : 2025-12-29 DOI: 10.1016/j.nepr.2025.104697

Hayden Astbury , Emily Fortune , Cory Dal Ponte , Kayley Lyons , Louise SHAW

Aim

To review the literature on generative artificial intelligence for teaching and assessment in health professions education

Background

Advancements in generative artificial intelligence (GenAI), such as ChatGPT, offer new possibilities for health professions education. These technologies offer potential benefits in teaching and assessment, including personalised learning and automated resource generation. Despite its potential, concerns about accuracy, ethics and reliability remain. This scoping review examines GenAI’s implementation, benefits and challenges in teaching and assessment across health professions education.

Design

Scoping review.

Methods

Following Arksey and O’Malley’s five-stage framework, with refinements based on the Joanna Briggs Institute (JBI) methodology, Medline, CINAHL and Web of Science Core Collection were searched for peer-reviewed studies published between January 2019 and June 2024. Studies were screened independently by two reviewers and data extraction performed systematically to ensure consistency.

Results

Studies (n = 5826) were assessed for eligibility, with 23 meeting the inclusion criteria. All included studies were published in 2023 and 2024. The primary applications of GenAI were in learning resource development and assessment, with reported benefits such as time savings, personalised learning and reduced resource use. Challenges included accuracy concerns, inconsistent outputs, technical limitations, algorithmic bias and risks to academic integrity.

Conclusions

This scoping review provides an overview of how GenAI is being integrated into health professions education. While the technology offers opportunities to enhance teaching and assessment, its implementation requires consideration of reliability, ethical concerns and educator preparedness. This review is the first to examine GenAI implementation across multiple AHPRA-regulated health professions and proposes a practical framework (AI HPE checklist) to guide responsible use.

目的综述生成性人工智能在卫生专业教育教学与评估中的研究进展。背景生成性人工智能（GenAI）的发展为卫生专业教育提供了新的可能性，如ChatGPT。这些技术为教学和评估提供了潜在的好处，包括个性化学习和自动化资源生成。尽管它有潜力，但对准确性、伦理和可靠性的担忧仍然存在。本范围审查审查了GenAI在整个卫生专业教育的教学和评估方面的实施、益处和挑战。DesignScoping审查。方法根据Arksey和O 'Malley的五阶段框架，并根据乔安娜布里格斯研究所（JBI）的方法进行了改进，检索了Medline、CINAHL和Web of Science Core Collection，检索了2019年1月至2024年6月期间发表的同行评议研究。研究由两名审稿人独立筛选，并系统地进行数据提取以确保一致性。结果纳入研究（n = 5826），其中23项符合纳入标准。所有纳入的研究都发表于2023年和2024年。GenAI的主要应用是在学习资源开发和评估方面，据报道，它的好处包括节省时间、个性化学习和减少资源使用。挑战包括准确性问题、不一致的产出、技术限制、算法偏见和学术诚信风险。结论：本综述概述了GenAI如何被纳入卫生专业教育。虽然这项技术提供了加强教学和评估的机会，但它的实施需要考虑可靠性、道德问题和教育工作者的准备。本综述首次检查了在多个ahpra监管的卫生专业中GenAI的实施情况，并提出了一个实用框架（AI HPE清单）来指导负责任的使用。

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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Nurse Education in Practice

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