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Podcasting is used in higher education so various digital resources can be shared with students. This review aims to synthesise evidence on podcasting in nursing and midwifery education. PubMed, MEDLINE, CINAHL, Scopus and ERIC databases were searched using key terms. 242 articles were found and screened. Data extraction, quality assessment and data analysis, underpinned by a Social Media Learning Model, were conducted on relevant studies. Twenty-six studies were included in the review. Three themes emerged; 1) learning and other outcomes, 2) antecedents to learning, and 3) learning process. Students seemed to acquire new knowledge and skills by using podcasts and it also appeared to improve clinical confidence. The organisation of podcasting, digital literacy and e-Professionalism, the personal motivation of learners, and flexible access to the technology seemed to impact the delivery of this educational intervention. Mechanisms that appeared to affect the learning process were the speed of exchange, the type of social media user, the timeframe, quality of information, the functionality of podcasts and other learning activities. This review synthesised evidence on podcasting in nursing and midwifery education. The technology was seen as a positive learning tool but more robust research examining its efficacy in improving learning outcomes is needed. •Podcasting is being used in nursing and midwifery education to support learning.•Review findings suggest podcasting may improve learning outcomes.•Newer generations of students seem to like technology enhanced learning resources.•More robust studies are needed to determine the efficacy of this pedagogical tool.•The Social Media Learning Model could help inform future teaching and learning.

Large language models (LLMs) are increasingly used in nursing education, yet their conceptual foundations remain abstract and underexplored. This concept analysis addresses the need for clarity by examining the relevance, meaning, contextual applications, and defining attributes of LLMs in nursing education, using Rodgers' evolutionary method. This paper aims to explore the evolutionary concept of LLMs in nursing education by providing a concept analysis through a comprehensive review of the existing published literature. Rodgers' evolutionary concept analysis method was used. PubMed, CINAHL, PsycINFO, Scopus, and Google Scholar were used to search for relevant publications. A total of 41 papers were included based on inclusion criteria that focused on studies published in English within the last 5 years to ensure relevance to the current use of LLMs exclusively in nursing education. Studies were excluded if they focused on clinical nursing applications, were not available in English, lacked full-text accessibility, or examined other artificial intelligence (AI) technologies unrelated to LLMs (eg, robotics). As a result of this analysis, a proposed definition of LLMs in nursing education has been developed, describing them as accessible, personalized, innovative, and interactive tools that create revolutionary learning experiences, often leading to enhanced cognitive and skill development and improvement in learning and teaching quality. This concept analysis highlights LLMs' transformative potential to enhance access to resources, support individualized learning, and augment nursing education. While promising, careful attention must be given to their limitations and ethical implications, ensuring their integration aligns with the values and goals of nursing education, particularly in specialized areas such as graduate nursing programs.

As artificial intelligence (AI) technology permeates health care settings, nurse leaders must position themselves to shape its development, implementation, and impact, guiding meaningful change that benefits nurses and care delivery. Nurse leaders possess the capacity to influence decisions, shape practice, and ensure the delivery of ethical, safe, and high-quality care. While AI technology is reshaping many aspects of health care delivery, there is limited knowledge on how nurse leaders perceive and experience this shift. This scoping review aimed to explore the intersection of nursing leadership and AI technology in health care by mapping current evidence, identifying key concepts, and highlighting knowledge gaps within the literature. This scoping review was guided by the Joanna Briggs Institute methodology and reported on using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. A systematic search of 4 electronic databases (CINAHL [EBSCO Information Services], Ovid MEDLINE [Wolters Kluwer], PsycINFO [American Psychological Association], and Scopus [Elsevier]) was conducted for English-language, peer-reviewed literature published between 2014 and 2025. Gray literature was also reviewed. Articles were included if they met the inclusion criteria by exploring the population of nurse leaders and the concept of AI technology within the context of health care settings and were published in English from May 2014 forward. A total of 26 articles were included in the analysis. Qualitative content analysis and numerical summary supported the inductive identification and synthesis of data categories. Of the 26 articles included, 8 were empirical (qualitative, quantitative, or mixed methods), and 18 were conceptual or theoretical articles. Although 1 article was Canadian, there were no empirical studies conducted by Canadian researchers. The qualitative content analysis of the primary search findings revealed 6 overarching data categories: (1) leading digital transformation and technology integration, (2) AI technology and the nursing role: reshaping practice, (3) ethical considerations of AI technology for nurse leaders, (4) AI technology as a facilitator of innovative leadership, (5) education and training on AI technology in nursing practice, and (6) influence of AI technology on the work environment. This review confirms that nurse leaders play an essential role in shaping the future of health care in the context of AI technology. Although this review highlights a growing recognition of nursing leadership as a crucial driver of AI technology integration in health care, there is a lack of research to guide practice, policy, and leadership development through education, despite emerging interest and a recent increase in empirical work. The findings accentuate the need for increased investment in nurse-led research and leadership development to ensure that AI systems are designed, implemented, and evaluated in a manner that upholds ethical care, equity, and professional nursing values. As health care systems increasingly adopt AI technology, nurse leaders must be equipped with the knowledge, tools, and support required to lead transformative change and act as AI technology directors.

Transformation into a digitally enabled profession will maximize the benefits to patient care, write Richard Booth and colleagues

It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. Increasingly, researchers are exploring the potential influences of AI health technologies (AIHTs) on nursing in general and on nursing education more specifically. However, little emphasis has been placed on synthesizing this body of literature. A scoping review was conducted to summarize the current and predicted influences of AIHTs on nursing education over the next 10 years and beyond. This scoping review followed a previously published protocol from April 2020. Using an established scoping review methodology, the databases of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest were searched. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI. Data were charted using a structured form and narratively summarized into categories. A total of 27 articles were identified (20 expository papers, six studies with quantitative or prototyping methods, and one qualitative study). The population included nurses, nurse educators, and nursing students at the entry-to-practice, undergraduate, graduate, and doctoral levels. A variety of AIHTs were discussed, including virtual avatar apps, smart homes, predictive analytics, virtual or augmented reality, and robots. The two key categories derived from the literature were (1) influences of AI on nursing education in academic institutions and (2) influences of AI on nursing education in clinical practice. Curricular reform is urgently needed within nursing education programs in academic institutions and clinical practice settings to prepare nurses and nursing students to practice safely and efficiently in the age of AI. Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education. Finally, nursing students and practicing nurses must be equipped with the requisite knowledge and skills to effectively assess AIHTs and safely integrate those deemed appropriate to support person-centered compassionate nursing care in practice settings. RR2-10.2196/17490.

Background Team-based care is a primary care model that involves collaborative care between primary care providers and other professional providers. In Ontario Canada, this model is delivered through Community Health Centres. The study objective was to identify if visits to different providers within Community Health Centres were associated with fewer emergency department visits. Methods We conducted a retrospective population-based, nested case-control study using healthcare data at ICES. We included adults receiving primary care at a Community Health Centre in Ontario between 2016 and 2018 and captured the outcome of emergency department visits between 2019 and 2020. Using the Generalized Estimating Equation (GEE) extension of the negative binomial regression model, we estimated the association between number of visits to seven different provider types within a Community Health Centre and number of emergency department visits, with adjustment for potential confounders. We defined team-based care as a visit to one of the providers in a group other than a primary care physician or nurse practitioner (i.e., mental health, diet and lifestyle education, community workers/ lay patient support, physical therapy, health promotion system navigation, foot care). We also calculated the ratio of observed-to-expected average emergency department visits per person. Results We identified 138,324 patients across 71 Community Health Centres, with 43% who received team-based care. We found that increased number of visits to any provider was associated with more emergency department visits. However, there were no significant differences between observed and expected number of emergency department visits for people who received care from different providers. Conclusions Almost half of the adult Community Health Centre population received team-based care. Although some benefits of team-based care have been established in the literature, we did not find any associations between care received from individual team-based provider types with lower emergency department visits.

Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses-the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers. This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond. Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized. A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI. Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies. RR2-10.2196/17490.

BackgroundSuicide in older adults is a significant overlooked problem worldwide. This is especially true in Canada where a national suicide prevention strategy has not been established. MethodsUsing linked health-care administrative databases, this population-level study (2011 to 2015) described the incidence of older adult suicide (aged 65+), and identified clinical and socio-demographic factors associated with suicide deaths. ResultsThe findings suggest that suicide remains a persistent cause of death in older adults, with an average annual suicide rate of about 100 per million people over the five-year study per-iod. Factors positively associated with suicide vs. non-suicide death included being male, living in rural areas, having a mental illness, having a new dementia diagnosis, and hav-ing increased emergency department visits in the year prior to death; whereas, increased age, living in long-term care, having one or more chronic health condition, and increased interactions with primary health care were negatively associ-ated with a suicide death. ConclusionFactors associated with suicide death among older adults highlighted in this study may provide better insights for the development and/or improvement of suicide prevention pro-grams and policies.

INTRODUCTION People experiencing homelessness are disproportionately affected by dementia, yet little is known about their dementia care and mortality rates after a diagnosis. METHODS Homeless (n = 559) and housed (n = 2002) individuals newly diagnosed with dementia were matched on age, sex, diagnosis date, and health region within the province of Ontario, Canada. Dementia care, long‐term care admissions, health service use, and mortality rates within 1 year of diagnosis were compared between groups. RESULTS Homeless individuals were more often admitted to long‐term care and less often received cholinesterase inhibitors. They also had higher rates of unscheduled emergency department visits, hospital bed days without acute care needs, and mortality compared to housed individuals. DISCUSSION Individuals experiencing homelessness have higher use of hospital‐based services and elevated mortality. They are also more frequently admitted to long‐term care, reinforcing the importance of developing integrated care models that combine health care, social services, and housing support. Highlights Homeless individuals diagnosed with dementia face higher mortality and care gaps. Most are not placed in long‐term care within a year of diagnosis. Tailored care models linking health care, housing, and social services are needed.

The rapid integration of artificial intelligence (AI) into nursing practice offered transformative potential to enhance clinical processes, improve patient outcomes, and optimize workflows. However, the ethical challenges posed by AI technologies have outpaced the development of corresponding nursing frameworks. This concept analysis articulates a contemporary definition of AI‐integrated nursing ethics and presents its defining attributes through Walker and Avant’s eight‐step analytical framework. The analysis identified four core attributes that align with the ethical principles of nursing and operational attributes of AI: (1) nurses must maintain responsibility for clinical decisions even when AI recommendations are involved; (2) a privacy impact assessment of AI systems should be conducted regularly to evaluate potential risks and continually enhance information protection; (3) nurses must be able to understand and communicate AI system recommendations and the reasoning processes; and (4) nurses must identify and address AI‐related bias to advocate for equitable care, while AI systems should be programmed to incorporate social and cultural aspects. Collectively, these attributes position AI‐integrated nursing ethics to enhance nurses’ ethical judgment and clinical expertise. The findings also emphasize the necessity of advancing nursing practice, research, policy, and education for responsible AI integration. Recommendations include developing evidence‐based guidelines, updating institutional explainability policies, integrating AI ethics into nursing curricula, and further research on bias mitigation. By addressing these areas, the nursing profession can ensure that AI integration aligns with its core ethical principles, enhancing patient care while preserving the integrity of nursing judgment.