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Internet is women's primary source of information about cervical cancer and human papillomavirus. The aim of this study was to determine the associations of electronic health literacy with cervical cancer and human papillomavirus awareness among women of reproductive age. This is a cross-sectional study. The research sample consisted of 330 women of reproductive age (15-49 years), who were admitted to family health centers. The data were collected between July and August 2023 using eHealth Literacy Scale and the Cervical Cancer and Human Papillomavirus Awareness Questionnaire. Multiple linear regression analysis was performed to explore the predictors of cervical cancer and human papillomavirus awareness. In this study, the mean score of women's knowledge about cervical cancer and human papillomavirus was found to be low (4.54 ± 3.94), and the mean score of threat perception was found to be moderate (45.60 ± 6.54). eHealth literacy was found to be a predictor of women's knowledge about cervical cancer and human papillomavirus and threat perception. This result suggests that eHealth literacy should be considered for interventions to increase knowledge and awareness of women about cervical cancer and human papillomavirus. 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. The aim of this study was to determine the effect of real-time videos with smart glasses on the performance of cardiopulmonary resuscitation performed by nursing students. In this randomized controlled pilot study, the students were randomly assigned to the smart glass group (n = 12) or control group (n = 8). Each student's cardiopulmonary resuscitation performance was evaluated by determining sequential steps in the American Heart Association algorithm they applied and the accuracy and time of each step. A higher number of participants correctly checked response breathing, requested a defibrillator, activated the emergency response team, and provided appropriate chest compressions and breaths in the smart glass group than the control group. There were significant differences between groups. Furthermore, more participants significantly corrected chest compression rate and depth and hand location, used a defibrillator, and sustained cardiopulmonary resuscitation until the emergency response team arrived in the smart glass group than in the control group. Additionally, a significantly shorter time was observed in the smart glass group than in the control group in all variables except time to activate the emergency response team (P This descriptive study aims to investigate the content, quality, and reliability of YouTube videos containing content related to endotracheal tube aspiration. The study was scanned using the keywords “endotracheal aspiration” and “endotracheal tube aspiration,” and 22 videos were included in the study. The contents of the selected videos were measured using the Endotracheal Tube Aspiration Skill Form, their reliability was measured using the DISCERN Survey, and their quality was measured using the Global Quality Scale. Of the 22 videos that met the inclusion criteria, 18 (81.8%) were educational, and four (18.2%) were product promotional videos. When pairwise comparisons were made, the coverage score of open aspiration videos was higher for educational videos than for product promotion videos (P No abstract available 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. Today, with the enhancement in the usage of smartphones, the concepts of nomophobia and phubbing have emerged. Nomophobia refers to the fear of being deprived of smartphones/smart devices. Phubbing is the use of a person's smartphone in situations that are not appropriate for the situation, time, and place. Therefore, the study purposed to evaluate nursing students' nomophobia and phubbing scores in Turkey, Portugal, and the United States. The data were collected with the Personal Information Questionnaire, Nomophobia Scale, and Phubbing Scale from N = 446 nursing students. The mean age of the students was 22.04 ± 4.08 years, and 86.5% were women. It was found that the total nomophobia scores of the nursing students were 80.15 ± 21.96, 72.29 ± 28.09, and 99.65 ± 6.11, respectively in Turkey, Portugal, and the United States. When the countries' Nomophobia Scale total scores, “giving up convenience,” “not being able to communicate,” and “losing connectedness” scores were compared with each other, they were found to be statistically significant (P No abstract available Empathy is significant in nursing, and showing empathy toward a patient positively impacts a patient's health. Learning empathy through immersive simulations is effective. Immersion is an essential factor in virtual reality. This study aimed to describe nursing students' experiences of empathy in a virtual reality simulation game. Data were collected from nursing students (n = 20) from May 2021 to January 2022. Data collection included individual semistructured interviews; before the interviews, the virtual reality gaming procedure was conducted. Inductive content analysis was used. Nursing students experienced compassion and a feeling of concern in the virtual reality simulation game. Students were willing to help the virtual patient, and they recognized the virtual patient's emotions using methods such as listening and imagining. Students felt the need to improve the patient's condition, and they responded to the virtual patient's emotions with the help of nonverbal and verbal communication and helping methods. Empathy is possible to experience by playing virtual reality simulation games, but it demands technique practicing before entering the virtual reality simulation game. The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, the review aimed to explore whether implementing these interventions as bundles reduced practice pattern variance and positively impacted patient care outcomes for inpatients with T2DM. Six databases were searched for articles published from January 1, 2000, to January 1, 2024. Nine studies met criteria and were summarized by aims, outcome measures, clinical or practice implications, AI/ML model types, study variables, and AI/ML model outcomes. A variety of AI/ML models were used. Multiple data sources were leveraged to train the models, resulting in varying impacts on practice patterns and outcomes. Studies included aims across 4 thematic areas to address: therapeutic patterns of care, analysis of treatment pathways and their constraints, dashboard development for clinical decision support, and medication optimization and prescription pattern mining. Multiple disparate data sources (i.e., prescription payment data) were leveraged outside of those traditionally available within EHR databases. Notably missing was the use of holistic multidisciplinary data (i.e., nursing and ancillary) to train AI/ML models. AI/ML can assist in identifying the appropriateness of specific interventions to manage diabetic care and support adherence to efficacious treatment pathways if the appropriate data are incorporated into AI/ML design. Additional data sources beyond the EHR are needed to provide more complete data to develop AI/ML models that effectively discern meaningful clinical patterns. Further study is needed to better address nursing care using AI/ML to support effective inpatient diabetes management. We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians' burden. Although such technologies can be helpful in applications such as patient education, drafting responses to patient questions and emails, medical record summarization, and medical research facilitation, there are concerns about the tools' readiness for use within the healthcare domain and acceptance by the current workforce. The goal of this article is to provide nurses with an understanding of the currently available foundation models and artificial intelligence tools, enabling them to evaluate the need for such tools and assess how they can impact current clinical practice. This will help nurses efficiently assess, implement, and evaluate these tools to ensure these technologies are ethically and effectively integrated into healthcare systems, while also rigorously monitoring their performance and impact on patient care. Knee arthroplasty surgery, which is increasingly performed due to increased life expectancy, has positive outcomes, although it can also cause serious health problems following surgery. This study was conducted to evaluate the impact of patient-related education via a QR code on total knee arthroplasty problems and emergency department referral rates. Participants were randomly assigned to intervention (n = 51) and control (n = 51) groups. The intervention group received QR code–supported training. The outcomes were assessed at baseline (preoperative), discharge, and postoperative sixth week. In the intervention group, significantly fewer problems related to total knee arthroplasty occurred at discharge and in week 6, and a higher level of functionality was noted (P Improving nurses' situation awareness skills would likely improve patient status recognition and prevent adverse events. Technologies such as electronic health record dashboards can be a promising approach to support nurses' situation awareness. However, the effect of these dashboards on this skill is unknown. This systematic literature review explores the evidence around interventions to improve nurses' situation awareness at the point of care. Current research on this subject is limited. Studies that examined the use of electronic health record dashboards as an intervention had weak evidence to support their effectiveness. Other interventions, including communication interventions and structured nursing assessments, may also improve situation awareness, but more research is needed to confirm this. It is important to carefully consider the design and content of situation awareness interventions, as well as the specific outcomes being measured, when designing situation awareness interventions. Overall, there is a need for higher-quality research in this area to determine the most effective interventions for improving nurse situation awareness. Future studies should focus on developing dashboards that follow a theoretical situation awareness model information and represent all situation awareness levels. Errors in decision making and communication play a key role in poor patient outcomes. Safe patient care requires effective decision making during interdisciplinary communication through communication channels. Research on factors that influence nurse and physician decision making during interdisciplinary communication is limited. Understanding influences on nurse and physician decision making during communication channel selection is needed to support effective communication and improved patient outcomes. The purpose of the study was to explore nurse and physician perceptions of and decision-making processes for selecting interruptive or noninterruptive interdisciplinary communication channels in medical-surgical and intermediate acute care settings. Twenty-six participants (10 RNs, 10 resident physicians, and six attending physicians) participated in semistructured interviews in two acute care metropolitan hospitals for this qualitative descriptive study. The Practice Primed Decision Model guided interview question development and early data analysis. Findings include a core category, Development of Trust in the Communication Process, supported by three main themes: (1) Understanding of Patient Status Drives Communication Decision Making; (2) Previous Interdisciplinary Communication Experience Guides Channel Selection; and (3) Perceived Usefulness Influences Communication Channel Selection. Findings from this study provide support for future design and research of communication channels within the EHR and clinical decision support systems. No abstract available Decision support systems have been widely used in healthcare in recent years; however, there is lack of evidence on global trends and hotspots. This descriptive bibliometric study aimed to analyze bibliometric patterns of decision support systems in nursing. Data were extracted from the Web of Science Core Collection. Published research articles on decision support systems in nursing were identified. Co-occurrence and co-citation analysis was performed using CiteSpace version 6.1.R2. In total, 165 articles were analyzed. A total of 358 authors and 257 institutions from 20 countries contributed to this research field. The most productive authors were Andrew Johnson, Suzanne Bakken, Alessandro Febretti, Eileen S. O'Neill, and Kathryn H. Bowles. The most productive country and institution were the United States and Duke University, respectively. The top 10 keywords were “care,” “clinical decision support,” “clinical decision support system,” “decision support system,” “electronic health record,” “system,” “nursing informatics,” “guideline,” “decision support,” and “outcomes.” Common themes on keywords were planning intervention, national health information infrastructure, and methodological challenge. This study will help to find potential partners, countries, and institutions for future researchers, practitioners, and scholars. Additionally, it will contribute to health policy development, evidence-based practice, and further studies for researchers, practitioners, and scholars. Paramount to patient safety is the ability for nurses to make clinical decisions free from human error. Yet, the dynamic clinical environment in which nurses work is characterized by uncertainty, urgency, and high consequence, necessitating that nurses make quick and critical decisions. The aim of this study was to examine the influence of human and environmental factors on the decision to administer among new graduate nurses in response to alert generation during bar code–assisted medication administration. The design for this study was a descriptive, longitudinal, observational cohort design using EHR audit log and administrative data. The study was set at a large, urban medical center in the United States and included 132 new graduate nurses who worked on adult, inpatient units. Research variables included human and environmental factors. Data analysis included descriptive and inferential analyses. This study found that participants continued with administration of a medication in 90.75% of alert encounters. When considering the response to an alert, residency cohort, alert category, and previous exposure variables were associated with the decision to proceed with administration. It is important to continue to study factors that influence nurses' decision-making, particularly during the process of medication administration, to improve patient safety and outcomes. Knowledge models inform organizational behavior through the logical association of documentation processes, definitions, data elements, and value sets. The development of a well-designed knowledge model allows for the reuse of electronic health record data to promote efficiency in practice, data interoperability, and the extensibility of data to new capabilities or functionality such as clinical decision support, quality improvement, and research. The purpose of this article is to describe the development and validation of a knowledge model for healthcare-associated venous thromboembolism prevention. The team used FloMap, an Internet-based survey resource, to compare metadata from six healthcare organizations to an initial draft model. The team used consensus decision-making over time to compare survey results. The resulting model included seven panels, 41 questions, and 231 values. A second validation step included completion of an Internet-based survey with 26 staff nurse respondents representing 15 healthcare organizations, two electronic health record vendors, and one academic institution. The final knowledge model contained nine Logical Observation Identifiers Names and Codes panels, 32 concepts, and 195 values representing an additional six panels (groupings), 15 concepts (questions), and the specification of 195 values (answers). The final model is useful for consistent documentation to demonstrate the contribution of nursing practice to the prevention of venous thromboembolism. 
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