Explore the vast range of titles available.

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.

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.

One-dimensional (1D) supramolecular polymers are commonly found in natural and synthetic systems to prompt functional responses that capitalise on hierarchical molecular ordering. Despite amphiphilic self-assembly being significantly studied in the context of aqueous encapsulation and autopoiesis, very little is currently known about the physico-chemical consequences and functional role of 1D supramolecular polymerisation confined in aqueous compartments. Here, we describe the different phenomena that resulted from the chemically triggered supramolecular fibrillation of synthetic peptide amphiphiles inside water microdroplets. The confined connection of suitable dormant precursors triggered a physically autocatalysed chemical reaction that resulted in functional environmental responses such as molecular uptake, fusion and chemical exchange. These results demonstrate the potential of minimalistic 1D supramolecular polymerisation to modulate the behaviour of individual aqueous entities with their environment and within communities. One-dimensional (1D) supramolecular polymers are commonly found in natural and synthetic systems but very little is currently known about the physico-chemical consequences and functional role of 1D supramolecular polymerisation confined in aqueous compartments. Here, the authors describe the different phenomena that resulted from the chemically triggered supramolecular fibrillation of synthetic peptide amphiphiles inside water microdroplets.

Fabrication of compartmentalised chemical systems with nested architectures and biomimetic properties has important implications for controlling the positional assembly of functional components, spatiotemporal regulation of enzyme cascades and modelling of proto-organelle behaviour in synthetic protocells. Here, we describe the spontaneous capture of glucose oxidase-containing proteinosomes in pH-sensitive fatty acid micelle coacervate droplets as a facile route to multi-compartmentalised host–guest protocells capable of antagonistic chemical and structural coupling. The nested system functions co-operatively at low-substrate turnover, while high levels of glucose give rise to pH-induced disassembly of the droplets, release of the incarcerated proteinosomes and self-reconfiguration into spatially organised enzymatically active vesicle-in-proteinosome protocells. Co-encapsulation of antagonistic enzymes within the proteinosomes produces a sequence of self-induced capture and host–guest reconfiguration. Taken together, our results highlight opportunities for the fabrication of self-reconfigurable host–guest protocells and provide a step towards the development of protocell populations exhibiting both synergistic and antagonistic modes of interaction. Multi-compartmentalised soft micro-systems are used as models of synthetic protocells. Here, the authors developed nested host–guest protocell constructs capable of self-reconfiguration in response to changes in pH generated by antagonistic modes of enzyme-mediated coupling.

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.

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.

The respiratory health of preweaned calves is an important determinant of their health, welfare, and future performance. This prospective cohort study measured bovine respiratory disease (BRD) on 16 dairy farms, including 476 calves in South-west England. Wisconsin and California respiratory scoring and thoracic ultrasonography were performed repeatedly at 7 ± 0.89 day intervals (mean ± SD) at 0–56 days of age (n = 3344 examinations). Cases were localized to the upper or lower respiratory tract, or both, and classified as new, repeat, or chronic. Prevalence and incidence were calculated. Multivariate modeling of factors associated with repeated measurements was performed. Increasing age (OR = 1.05, 95% CI 1.04–1.06) and fecal score (Score 2, OR = 1.78, 95% CI 1.14–2.77) were associated with a lower odds of a healthy BRD subtype, whereas increasing serum total protein (OR = 0.97, 95% CI 0.96–0.99) was protective. Older (OR 1.08, 95% CI 1.06–1.09), male (OR 1.69, 95% CI 1.01–2.84) calves with elevated Wisconsin respiratory scores (≥5, OR 5.61, 95% CI 3.38–9.30) were more likely to have elevated thoracic ultrasound scores. BRD remains common in calves born in UK dairy herds, requiring precise identification and management if preweaning health is to be optimized.