Technological advancements have led to the use of robots as prospective partners to complement understaffing and deliver effective care to patients. This article discusses relevant concepts on robots from the perspective of nursing theories and robotics in nursing and examines the distinctions between human beings and healthcare robots as partners and robot development examples and challenges. Robotics in nursing is an interdisciplinary discipline that studies methodologies, technologies, and ethics for developing robots that support and collaborate with physicians, nurses, and other healthcare workers in practice. Robotics in nursing is geared toward learning the knowledge of robots for better nursing care, and for this purpose, it is also to propose the necessary robots and develop them in collaboration with engineers. Two points were highlighted regarding the use of robots in health care practice: issues of replacing humans because of human resource understaffing and concerns about robot capabilities to engage in nursing practice grounded in caring science. This article stresses that technology and artificial intelligence are useful and practical for patients. However, further research is required that considers what robotics in nursing means and the use of robotics in nursing.

The awareness of care provided by stroke care unit (SCU) nurses in Japan to patients with an acute cerebrovascular accident (CVA) and the characteristic differences in their actual nursing practice were evaluated. A cross-sectional web-based questionnaire survey was administered to 1040 SCU nurses. Data collection and reporting procedures followed the STROBE Statement Checklist for cross-sectional studies. Exploratory factor analysis, using 52 observation items, identified eight factors with a factor loading > 0.4. For all factors, the actual practice was significantly lower than the awareness of the importance of nursing care for patients with acute CVA. Awareness and actual practice of recognition of patients’ physical changes (RPPCs) were high. The actual practice of RPPCs and preventing the worsening of acute stroke and related symptoms varied, depending on years of experience in acute phase stroke care. RPPCs in actual practice had a significantly higher score among certified nurses or certified nurse specialists. Their awareness of the importance of collaborating with therapists was low. On-the-job training can improve nurses’ competence and prevent worsening conditions in patients with CVA. An emphasis on enhancing practice experience toward patients with acute CVA and facilitating the deployment of certified nurses in SCUs can improve nursing care practice.

Background: Nurses as primary healthcare providers demonstrate quality nursing care through competencies with healthcare technologies, while nurse managers assume the primacy of managing quality healthcare in their respective care settings. However, little is known about perceptions of the influence of care technologies on their nursing practice. Objective: This study aimed to determine managers’ and staff nurses’ perceptions regarding the Technological Competency as Caring in Nursing (TCCN) theory in general hospitals in Japan. Methods: This study employed a cross-sectional survey design, with 421 participants selected using a stratified sampling method. Technological Competency as Caring in Nursing Instrument–Revised (TCCNI-R) was used for online data collection using Survey Monkey©. Data were analyzed using Welch’s t-test and ANOVA. Results: Nurses with years of experience within the range of 20 to less than 30 years showed the highest TCCNI-R scores among the two groups. Nurses who had received education on caring in nursing showed significant differences for Factor 2 (Technological Competency as Caring), that of expressing Technological Competency as Caring. Three other factors showed no significant difference, namely in Factor 1 (Nursing Expression as Caring), Factor 3 (Technology and Caring), and Factor 4 (Technological Knowing). However, the average scores of these factors were high, which reflect high professional ethics and occupational discipline and increased awareness of caring in nursing. It was also found that the nurse managers were more aware of the TCCN than were the staff nurses. The nurse managers were also more aware of providing care using technology, recognizing the need-to-know patient needs through technology and providing care to the ever-changing patient’s condition. Conclusion: The study discovered that continuing education is needed regarding the practice of nursing based on theory, enabling appropriate and accurate understanding of practicing knowing persons as caring in nursing.

The purpose of this article is to examine ethico-legal issues and existing Japanese law addressing interpersonal relationships in healthcare situations involving Humanoid Caring Robots (HCRs) and older adult patients. Potential safety issues include environmental situations between older adults and HCRs; identification of potential “leakage” of personal information from stored data in the cloud server; and issues of access authority for HCRs' stored data. It is necessary to have accurate findings supporting the legislation about HCRs to provide safe and effective care for older adults, and to limit healthcare facilities to reasonable risk level.

Background: The middle range theory Technological Competency as Caring in Nursing (TCCN) guides nursing practices. The TCCN Instrument (TCCNI) measures perception dimension of the theory and has been revised and translated into the Japanese language (TCCNI-R). Testing the translated version of the TCCNI-R to English language with the inclusion of a practice dimension is warranted. Purpose: This study aims to determine the psychometric properties of the TCCNI-Revised English version with Practice dimension (TCCNI-RePract). Methods: A web-based cross-sectional study was conducted with data from 202 valid questionnaire copies from professional nurses in selected hospitals and nurse educators in universities. Results: The suitability for factor analysis was determined using Kaiser-Meyer-Olkin index (0.93), Bartlett's sphericity test of 3256.93, p<0.001, the anti-image correlations ranged between 0.87 and 0.96, and an average value of communalities of 0.66. In the four rotations conducted with the maximum likelihood method with a Harris-Kaiser Orthoblique rotation, four items were excluded with factor loadings less than 0.40. These results determined the final scale with 21 items and four subscales, namely: (1) Knowing the person (8 items); (2) Technological competency as Caring (6 items); (3) Technology and caring (4 items); and (4) Expression of nursing as Caring (3 items). Cronbach’s alpha coefficient for the total scale was 0.94. With two dimensions of the TCCNI-RePract, the perception dimension had significantly higher scores than the practice dimension. When comparing mean factor point among the dimensions, the perception scores were significantly higher for Factor 1 and Factor 3. Conclusion:The TCCNI-RePract is an acceptable tool that can reliably measure nurses’ perception and practice of TCCN. It is affirmed that with this tool, measuring perception and practice status of TCCN theory is possible. It is considered that the evaluation results can be used to plan in-hospital education.

Frequent exposure to patient deaths prompts nurses to experience grief. Unresolved grief leads to harmful consequences of nurses’ mental health and quality of nursing care. A cross-sectional study using an online survey was conducted to determine the psychometric properties of the Grief traits and State Scale for Nurses. Exploratory factor analysis revealed two factors measuring the level of nurses’ grief traits (Cronbach’s alpha: 0.84) and two factors in grief state (Cronbach’s alpha: 0.86). Nurses’ feelings of unable to provide good care were associated with a higher risk of grief (odds ratio (OR): 4.30, 95% confidence interval (CI): 1.45–12.75), uncomfortable feeling toward deaths (OR: 11.29, 95%CI: 1.48–85.91), and emotional exhaustion (OR: 7.12, 95%CI: 1.63–30.99). Results indicated that the scale was reliable in determining the levels of their grief. Nurse managers can use the scale to identify their nurses’ levels of grief, creating opportunities to influence the resolution of the grief experiences.