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This study used text network analysis and topic modeling to examine the knowledge structure of family-centered care in neonatal ICU nurses. Text was extracted from abstracts of 110 peer-reviewed articles published between 1995 and 2023 and analyzed by identifying keywords, topics, and changes in research topics over time. Analysis of keywords revealed significant terms including “infant,” “family,” “experience,” “interventions,” and “parent participation,” highlighting family's central roles in family-centered care in neonatal ICU discourse. The research topics identified included “family-centered partnerships,” “barriers to implementing family-centered care,” “infant-mother attachment intervention,” “family participation intervention,” and “parenthood.” Over time, research on family-centered care in neonatal ICUs nurses has steadily increased, with notable increases in “family-centered partnerships” and “barriers to implementing family-centered care.” The findings underscore the evolving landscape of family-centered care in neonatal ICUs, emphasizing the critical role of collaborative care models in enhancing neonatal and familial outcomes. These insights provide a foundation for developing family-centered care programs that empower both nurses and families, supporting the holistic care of vulnerable infants. This study's results offer comprehensive insights into understanding family-centered care in the neonatal ICUs and could serve as a foundation for future studies to develop family-centered care programs for neonatal ICU nurses and families. Based on this study, it is recommended that nursing education programs integrate family-centered care training into their curricula, with an emphasis on communication, cultural competence, and family partnerships.
There are challenges involving human resource management, as the selection and evaluation processes for nursing diagnostic labels are time-consuming, resulting in an excessive workload. This, in turn, can lead to insufficient attention being given to patients' medical issues. As a proof of concept, to solve challenges related to nursing diagnoses, we developed an artificial neural network model using progress records and evaluated its performance. Specifically, datasets were obtained from progress record data from the critical care department system in Japan between 2014 and 2019 and the corresponding nursing diagnosis data from electronic medical records. The model was trained, and its performance was evaluated. We compared several methods for vectorizing progress records and evaluated performance with and without oversampling for imbalanced data. We used a naive Bayes classifier for comparison. The model using term frequency–inverse document frequency achieved the highest values for both accuracy and the area under the precision-recall curve across all target nursing diagnoses (accuracy = 0.705–0.911; area under the precision-recall curve = 0.387–0.929). The artificial neural network model outperformed the naive Bayes classifier in both accuracy and area under the precision-recall curve, which indicated its superiority as a classifier.
Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome is a threat to the patients' quality of life. This study aimed to evaluate the effectiveness of a clinical decision support system in postembolization syndrome management across nurses and patient outcomes. This study is a randomized controlled trial. We included 40 RNs and 51 hospitalized patients in the study. For nurses in the experimental group, a clinical decision support system and a handbook were provided for 6 weeks, and for nurses in the control group, only a handbook was provided. Notably, the experimental group exhibited statistically significant improvements in patient-centered caring attitude, pain management barrier identification, and comfort care competence after clinical decision support system implementation. Moreover, patients' symptom interference during the experimental period significantly decreased compared with before the intervention. This study offers insights into the potential of clinical decision support system in refining nursing practices and nurturing patient well-being, presenting prospects for advancing patient-centered care and nursing competence. The clinical decision support system contents, encompassing postembolization syndrome risk prediction and care recommendations, should underscore its role in fostering a patient-centered care attitude and bolster nurses' comfort care competence.
This study aimed to develop a virtual experiential application for respiratory patient care and evaluate its impact on nursing students' knowledge, self-efficacy, clinical practice anxiety, and performance confidence. This application with gamification elements was developed following a structured approach encompassing assessment, design, development, implementation, and evaluation. The experimental group consisted of 21 third-year university students who engaged with the application multiple times a day for 1 week; the control group, comprising 21 students, received traditional prelearning. Data were collected 1 week before and immediately before the clinical practice commencement, from March 7 to 24, 2023, using an online survey. Nursing knowledge, self-efficacy, clinical practice anxiety, and performance confidence were evaluated. Significant improvements were observed in the experimental group's knowledge of respiratory patient care, self-efficacy, clinical practice anxiety, and performance confidence. The application proved to be an effective learning resource and assisted students in implementing the nursing process to enhance patient conditions; it highlighted nursing educators' necessity in developing and evaluating educational content. The developed application was effective in enhancing student nurses' competence and confidence, affecting nursing education and patient care.
The integration of artificial intelligence such as ChatGPT into educational frameworks marks a pivotal transformation in teaching. This quasi-experimental study, conducted in September 2023, aimed to evaluate the effects of artificial intelligence–assisted learning on nursing students' ethical decision-making and clinical reasoning. A total of 99 nursing students enrolled in a pediatric nursing course were randomly divided into two groups: an experimental group that utilized ChatGPT and a control group that used traditional textbooks. The Mann-Whitney U test was employed to assess differences between the groups in two primary outcomes: (a) ethical standards, focusing on the understanding and applying ethical principles, and (b) nursing processes, emphasizing critical thinking skills and integrating evidence-based knowledge. The control group outperformed the experimental group in ethical standards and demonstrated better clinical reasoning in nursing processes. Reflective essays revealed that the experimental group reported lower reliability but higher time efficiency. Despite artificial intelligence's ability to offer diverse perspectives, the findings highlight that educators must supplement artificial intelligence technology with strategies that enhance critical thinking, careful data selection, and source verification. This study suggests a hybrid educational approach combining artificial intelligence with traditional learning methods to bolster nursing students' decision-making processes and clinical reasoning skills.
This study aimed to examine the effectiveness of an infection control simulation using mixed reality, comparing simulation fidelity with a high-fidelity mannequin (MN) group and problem-based learning with written cases group. This study used a three-group pretest-posttest quasi-experimental design. Two universities with similar curricula were conveniently selected, and a total of 72 nursing students were recruited. Participants were randomly assigned to three groups of 24 each. In the final analysis, there were 22 participants in the mixed reality groups, 21 in the mannequin groups, and 23 in the problem-based learning with written cases groups. Data were analyzed using descriptive statistics and the χ2, Kruskal-Wallis, and Wilcoxon signed rank tests. The mixed reality groups had a significantly positive effect on clinical reasoning ability and clinical competence than the problem-based learning with written cases groups, whereas the mannequin groups had a significantly positive effect on clinical competence than the problem-based learning with written cases groups. Mixed reality simulation is an appropriate simulation method that enhances learning immersion, satisfaction, and self-confidence in simulation. Additionally, it can substitute for mannequin simulation in terms of clinical reasoning ability and clinical competence. This study suggests that it is important to the various approaches in simulation fidelity to diversely enhance the competency of nursing students in simulation outcomes.
New nurses must acquire accurate knowledge of medication administration, as it directly affects patient safety. This study aimed to develop a microlearning-based self-directed learning chatbot on medication administration for novice nurses. Furthermore, the study had the objective of evaluating the chatbot feasibility. The chatbot covered two main topics: medication administration processes and drug-specific management, along with 21 subtopics. Fifty-eight newly hired nurses on standby were asked to use the chatbot over a 2-week period. Moreover, we evaluated the chatbot's feasibility through a survey that gauged changes in their confidence in medication administration knowledge, intrinsic learning motivation, satisfaction with the chatbot's learning content, and usability. After using the chatbot, participants' confidence in medication administration knowledge significantly improved in all topics (P
There is an increasing need for highly accessible health management platforms for comprehensive symptoms of Parkinson disease. Mobile apps encompassing nonmotor symptoms have been rarely developed since these symptoms are often subjective and difficult to reflect what individuals actually experience. The study developed an app for comprehensive symptom management and evaluated its usability and feasibility. A single-group repeated measurement experimental design was used. Twenty-two participants used the app for 6 weeks. Monitoring of nonmotor symptoms, games to address motor symptoms, and medication management were incorporated in the app. Quantitative outcomes were self-assessed through an online questionnaire, and one-on-one telephone interviews were conducted to understand the user's point of view. The successful experience of self-monitoring had improved participants' self-efficacy (Z = −3.634, P
This study aimed to explore digital literacy among community-dwelling older adults in urban South Korea. A semistructured interview guide was developed using the Digital Competence ( 2.0 framework, which emphasizes the competencies for full digital participation in five categories: information and data literacy, communication and collaboration, content creation, safety, and problem-solving. The data were analyzed using combined inductive and deductive content analysis. Inductive analysis identified three main categories: perceived ability to use digital technology, responses to digital technology, and contextual factors. In the results of deductive analysis, participants reported varying abilities in using digital technologies for information and data literacy, communication or collaboration, and problem-solving. However, their abilities were limited in handling the safety or security of digital technology and lacked in creating digital content. Responses to digital technology contain subcategories of perception (positive or negative) and behavior (trying or avoidance). Regarding contextual factors, aging-related physical and cognitive changes were identified as barriers to digital literacy. The influence of families or peers was viewed as both a facilitator and a barrier. Our participants recognized the importance of using digital devices to keep up with the trend of digitalization, but their digital literacy was mostly limited to relatively simple levels.
Game-based virtual reality simulation programs can capitalize on the advantages of non–face-to-face education while effectively stimulating the interest of trainees and improving training efficiency. This study aimed to develop a game-based virtual reality simulation program for nervous system assessment and to evaluate the effects of the program on the learning attitudes of nursing students. Using a one-group pretest-posttest design, 41 senior nursing students were enrolled, and their learning attitudes (self-directed learning attitude, academic self-efficacy, flow-learning experience, and learning presence) were evaluated. The effect of the program was statistically significant in self-directed learning attitude (t = −2.27, P = .027) and learning presence (t = −3.07, P = .003), but the difference was not statistically significant in academic self-efficacy (t = −1.97, P = .054) and learning flow (t = −0.74, P = .459). The virtual gaming simulation program can be used to effectively replace field training in situations wherein field training is limited, such as during the COVID-19 pandemic.
Mobile healthcare has emerged as a prominent technological solution for self-management of health. However, the development and utilization of tailored mobile healthcare applications for older adults with diabetes mellitus remain limited. This study examined the relationship between social support and mobile healthcare use and further explored how this relationship varies with eHealth literacy and application design among older adults with diabetes mellitus. A descriptive cross-sectional trial was conducted with a structured self-report questionnaire, surveying 252 South Korean older adults with diabetes mellitus via offline and online modes. The mediating effect and moderated mediating effect were analyzed with the PROCESS macro of SPSS. eHealth literacy mediated the relationship between social support and mobile healthcare use. High levels of eHealth literacy and social support may increase mobile healthcare use among older adults with diabetes. Application design aesthetics facilitated mobile healthcare use. Future researchers, healthcare providers, and developers can contribute to the development of tailored mobile healthcare applications for older adults with diabetes mellitus by considering application design aspects such as font size, color, and menu configuration.
This study aims to develop a virtual reality–based education program for managing behavioral and psychological symptoms of dementia for family carers of persons living with dementia and investigate the feasibility for users. The program was developed through literature review, interviews with family carers, surveys, and expert content validity assessment. User feasibility was evaluated quantitatively through a questionnaire on usefulness, ease of use, and satisfaction, and qualitatively through participant interviews. The program was produced in two parts, Type 1 and Type 2, consisting of three and six episodes, respectively. Participants showed a high level of satisfaction with overall program scores of 4.28 ± 0.66 and 4.34 ± 0.41 for the two evaluations. Participants also expressed that both programs were helpful, Type 1 for achieving changes in attitude associated with more understanding of persons living with dementia and Type 2 for acquiring coping methods through communication training. Use of the virtual reality device was not inconvenient and was identified as helpful due to the high immersion experience. Results of this study confirmed that family carers had no resistance to education using new technologies such as virtual reality devices and that virtual reality–based education could be effective for training family carers.
This study aimed to develop a Mobile Application to Prevent Recurrent Stroke to prevent recurrent stroke by enhancing self-management and to evaluate its effects on stroke survivors' health outcomes. The Mobile Application to Prevent Recurrent Stroke was developed based on social cognitive theory and the model in order of analysis, design, development, implementation, and evaluation process. The Mobile Application to Prevent Recurrent Stroke consisted of health management contents such as information about stroke, its associated risk factors, and required skills to conduct self-management with tailored support and counseling. A quasi-experimental preintervention and postintervention design was used involving a total of 54 stroke survivors. The experimental group (n = 27) was provided the Mobile Application to Prevent Recurrent Stroke for 8 weeks, whereas the control group (n = 27) received an education booklet. The result revealed that medication adherence (P = .002), healthy eating habit (P 
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