This study examined the published works related to healthcare robotics for older people using the attributes of health, nursing, and the human-computer interaction framework. An integrative literature review. A search strategy captured 55 eligible articles from databases (CINAHL, Embase, IEEE Xplore, and PubMed) and hand-searching approaches. Bibliometric and content analyses grounded on the health and nursing attributes and human-computer interaction framework were performed using MAXQDA. Finally, results were verified using critical friend feedback by a second reviewer. Most articles were from multiple authorship, published in non-nursing journals, and originating from developed economies. They primarily focused on applying healthcare robots in practice settings, physical health, and communication tasks. Using the human-computer interaction framework, it was found that older adults frequently served as the primary users while nurses, healthcare providers, and researchers functioned as secondary users and operators. Research articles focused on the usability, functionality, and acceptability of robotic systems. At the same time, theoretical papers explored the frameworks and the value of empathy and emotion in robots, human-computer interaction and nursing models and theories supporting healthcare practice, and gerontechnology. Current robotic systems are less anthropomorphic, operated through real-time direct and supervisory inputs, and mainly equipped with visual and auditory sensors and actuators with limited capability in performing health assessments. Results communicate the need for technological competency among nurses, advancements in increasing healthcare robot humanness, and the importance of conscientious efforts from an interdisciplinary research team in improving robotic system usability and utility for the care of older adults.

Background/Objectives: Brain-derived neurotrophic factor (BDNF) has been investigated as a potential mechanistic marker or therapeutic target to manage symptoms such as fatigue, pain, depression, and sleep disturbances. However, the variability in BDNF response to exercise or physical activity (exercise/PA) and its clinical relevance in symptom management remains unclear. This scoping review assesses existing studies exploring the relationships between exercise/PA, symptoms, and BDNF levels, specifically focusing on fatigue, pain, depression, and sleep disturbances in adults. Methods: Relevant studies indexed in PubMed and CINAHL were identified. Using systematic review software, two reviewers independently screened and evaluated full texts, based on the following criteria: human studies reporting BDNF levels in adults, using exercise/PA interventions, assessing symptoms (pain, fatigue, depression, and/or sleep disturbance) as outcomes, and published in English. Results: Of 950 records, 35 records met the inclusion criteria. While exercise/PA is broadly supported for managing symptoms, 74.3% (n = 26) of studies reported increased BDNF levels, and only 40% (n = 14) showed significant increases following exercise/PA. Only 14% (n = 5) of studies demonstrated a significant relationship between changes in BDNF and symptoms. No significant differences in BDNF levels and symptoms were observed between different types of exercise (e.g., aerobic vs. strength vs. flexibility/stretching) and PA. Conclusions: The current literature provides insufficient evidence to confirm BDNF as a marker for exercise/PA effectiveness on symptoms. Further clinical investigations are needed to validate its potential as a therapeutic target.

The advent of artificial intelligence (AI) in recent years has brought about transformative changes across various sectors, including healthcare. In nursing practice, education, and research, AI has the potential to revolutionize traditional methodologies, enhance learning experiences, and improve patient outcomes. Integrating AI tools and techniques can provide clinicians with smarter clinical solutions and nursing students with more robust and interactive learning environments, while also advancing research capabilities in the field. Despite the promising prospects, the incorporation of AI into nursing practice, education, and research presents several challenges. Firstly, there is a concern about the potential displacement of human roles in nursing due to automation, which may affect the human-centric nature of nursing care. Secondly, there are issues related to the lag in AI competency among nurses. Many current nursing curricula do not include comprehensive AI training, leading to a lack of preparedness in utilizing these technologies effectively. Lastly, the ethical implications of AI in healthcare, such as data privacy, patient consent, and the potential for biased algorithms, need to be meticulously addressed. To harness the full potential of AI in nursing practice, education, and research, several strategic actions including reinvesting in humanistic practice, revising core competencies and curriculum, and developing new ethical guidelines.

: The Fourth Industrial Revolution (4IR) generated considerable interests among scholars, informaticists and educational leaders around the globe. This industry shift brings with it exciting opportunities and inevitable challengesto various industries and professional practices including the health sciences. The purpose of the article is to illuminate the influences of the Fourth Industrial Revolution on the research,education and technology on Nursing and the Health Sciences. This article is organized as follows : the historical developments in the evolution of nursing images, industries and technologies in healthcare praxis, juxtaposing of current and impending trends and their impact on education, research and scholarship, and education in the healthcare sector. This article concludes with shared insights on several creative and proactive solutions in preparing for, creating new technologies, and mitigating the effects of the upcoming revolution. J. Med. Invest. 66 : 3-7, February, 2019

Interprofessional learning (IPL) is increasingly recognized as a promising strategy in addressing the burgeoning challenges in healthcare. Its assessment remains to be perplexing and requires accurate measurements. Thus, this study intended to develop a valid and reliable reflective tool in assessing IPL as a Continuing Professional Development (CPD) outcome. A one-group post-posttest pre-experimental design with tool development was employed to establish the validity and reliability of the “Inventory of Reflective Vignette–Interprofessional Learning” (IRV-IPL). This tool was developed from an extensive literature review and designed with three segments to assess interprofessional competencies before, after, and what if scenarios using vignettes. After it was validated by education experts (n = 5) and written consent forms were signed by the participants, the IRV-IPL was pilot tested among healthcare professionals (n = 10) for analysis and improvement. During the actual implementation, it was administered to healthcare professionals (n = 45) who participated in a university-provided CPD event. Collected data underwent validity and reliability testing. IRV-IPL generated excellent internal consistency (α = 0.98), and across all segments of collaboration (α = 0.96), coordination (α = 0.96), cooperation (α = 0.96), communication (α = 0.97), and commendation (α = 0.98). Items exhibited significantly positive large correlations (r > 0.35, p < 0.05) in all segments showing beneficial measures for postdictive validity in recalling prior interprofessional competencies, and predictive validity in estimating interprofessional learning as an outcome of CPD and alternative interventions. This study provided a piece of groundwork evidence on the use of IRV-IPL as a reflective assessment tool for interprofessional learning in CPD contexts. Further studies are essential to explore the educational utility of IRV framework in crafting relevant assessments and to establish construct validity of IRV-IPL using exploratory and confirmatory factor analyses.