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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. No abstract available No abstract available Dashboards display hospital quality and patient safety measures aimed to improve patient outcomes. Although literature establishes dashboards aid quality and performance improvement initiatives, research is limited from the frontline nurse manager's perspective. This study characterizes factors influencing hospital nurse managers' use of dashboards for unit-level quality and performance improvement with suggestions for dashboard design. Using a descriptive qualitative design, semistructured interviews were conducted with 11 hospital nurse managers from a health system in the Midwestern United States. Thematic analysis was used to describe four perceived factors influencing dashboard use: external, data, technology features, and personal. External factors included regulatory standards, professional standards of care, organizational expectations, and organizational resources. Data factors included dashboard data quality and usefulness. Technology features included preference for simple, interactive, and customizable visual displays. Personal factors included inherent nurse manager qualities and knowledge. Guidelines for dashboard design involve display of required relevant quality measures that are accurate, timely, useful, and usable. Future research should involve hospital nurse managers in user-centered design to ensure dashboards are favorable for use. Further, opportunities exist for nurse manager informatics training and education on dashboard use in preparation for their role and responsibilities in unit-level quality and performance improvement. 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 Medical errors, often resulting from miscommunication and cognitive lapses during handoffs, account for numerous preventable deaths and patient harm annually. This research examined nurses' perceived workload and cognitive load during handoffs on hospital units with varying patient acuity levels and patient-nurse ratios. Conducted at a southeastern US medical facility, the study analyzed 20 handoff dyads using the National Aeronautics and Space Administration Task Load Index to measure perceived workload and cognitive load. Linear regressions revealed significant associations between patient acuity levels, patient-nurse ratios, and National Aeronautics and Space Administration Task Load Index subscales, specifically mental demand (P = .007) and performance (P = .008). Fisher exact test and Wilcoxon rank sum test showed no significant associations between these factors and nurses' roles (P > .05). The findings highlight the need for targeted interventions to manage workload and cognitive load, emphasizing standardized handoff protocols and technological aids. The study underscores the variability in perceived workload and cognitive load among nurses across different units. Medical-surgical units showed higher cognitive load, indicating the need for improved workload management strategies. Despite limitations, including the single-center design and small sample size, the study provides valuable insights for enhancing handoff communications and reducing medical errors. Nursing students often experience anxiety, stress, and fear during a clinical rotation in a mental health setting due to stressors and biases toward the setting as well as lack experience in caring for patients with mental health conditions. One in four people worldwide suffers from a mental disorder; therefore, it is critical that nurses feel confident interacting with these patients to provide equitable care. Undergraduate training is a critical period for changing students' attitudes toward this population. This study's goal was twofold. First, we offered students’ exposure to common behaviors and symptoms displayed by a patient with mental illness through an engaging and immersive virtual reality simulation experience before taking care of patients in a clinical setting. Second, we aimed to determine if a virtual reality simulation will change students' attitude and stigma, favorably, toward patients with mental health conditions. We used a mixed-method comparative analysis to collect information and identify themes on undergraduate students’ attitudes and stigma toward patients with mental health conditions. Our findings demonstrate that virtual reality simulations enhance awareness and sensitivity to the situations of others (empathy) while improving their communication skills. The use of virtual reality in a baccalaureate curriculum deepens the understanding of health equity in behavioral health for nursing students. Background
Since the onset of the COVID-19 pandemic, healthcare workers around the world have experimented with technologies to facilitate communication and care for patients and their care partners.
Methods
Our team reviewed the literature to examine best practices in utilizing technology to support communication between nurses, patients, and care partners while visitation is limited. We searched four major databases for recent articles on this topic, conducted a systematic screening and review of 1902 articles, and used the Johns Hopkins Nursing Evidence-Based Practice for Nurses and Healthcare Professionals Model & Guidelines to appraise and translate the results of 23 relevant articles.
Results
Our evaluation yielded three main findings from the current literature: (1) Virtual contact by any technological means, especially video visitation, improves satisfaction, reduces anxiety, and is well-received by the target populations. (2) Structured video rounding provides effective communication among healthcare workers, patients, and offsite care partners. (3) Institutional preparation, such as a standardized checklist and dedicating staff to roles focused on facilitating communication, can help healthcare workers create environments conducive to therapeutic virtual communication.
Discussion
In situations that require healthcare facilities to limit visitation between patients and their care partners, the benefits of virtual visitation are evident. There is variance in the types of technologies used to facilitate virtual visits, but across all of them, there are consistent themes demonstrating the benefits of virtual visits and virtual rounding. Healthcare institutions can prepare for future limited-visitation scenarios by reviewing the current evidence and integrating virtual visitation into modern healthcare delivery. Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely about minimizing the prediction error. Existing models primarily utilize data such as historical patient visits, seasonal trends, holidays, and calendars. However, it is unclear what other features reduce the prediction error. Our systematic literature review identifies studies that use supervised machine learning to predict patient arrival numbers using nontemporal features, which are features not based on time or dates. We scrutinized 26 284 studies, eventually focusing on 27 relevant ones. These studies highlight three main feature groups: weather data, internet search and usage data, and data on (social) interaction of groups. Internet data and social interaction data appear particularly promising, with some studies reporting reduced errors by up to 33%. Although weather data are frequently used, its utility is less clear. Other potential data sources, including smartphone and social media data, remain largely unexplored. One reason for this might be potential data privacy challenges. In summary, although patient arrival prediction has become more important in recent years, there are still many questions and opportunities for future research on the features used in this area. Patient education and self-management are essential for patients with liver cirrhosis. Based on Fisher and Fisher's Information-Motivation-Behavior Skills model, a Cirrhosis Care App was developed to support the education and self-management of these patients. To evaluate the effectiveness of the application, a randomized controlled trial was conducted with patients having liver cirrhosis who were being followed up in the outpatient area of a medical center in Taiwan. The experimental group used the app for 1 month, whereas a control group continued to receive conventional patient education. A pretest and posttest questionnaire was used to evaluate the app's effectiveness in improving the knowledge and practice of self-care. In addition, a questionnaire was developed based on the Technology Acceptance Model to understand satisfaction with the app. Results showed that following the implementation of the Cirrhosis Care App, patients' self-care knowledge and ability to promote self-care practice improved. User satisfaction with the app was measured and reflected in its frequency of use. This study confirmed that the Cirrhosis Care App, based on the Information-Motivation-Behavior Skills model, can improve patient knowledge and self-care practice and be actively promoted to benefit patients with cirrhosis. Nurses faced numerous challenges during the pandemic, particularly with the increased burden of electronic documentation. Surges in patient volume and visits led to rapid changes in nursing documentation, prompting diverse responses from regulatory and healthcare organizations. Nurses expressed safety concerns and struggled with changes, calling for national standards and regulatory support. Policy relaxations, such as the 1135 Waiver, sparked debate on the future of nursing care plan documentation. Using mixed-methods exploratory design, the study identified modifications of nursing documentation during crises, commonalities in documentation burden reduction for applicability beyond pandemics, and consensus on the definition of “surge.” Documentation patterns were assessed from February to November 2022, involving 175 North American nurse leaders and informaticists. Data analysis included descriptive statistics, thematic analysis, and Pearson correlation coefficient. Significant differences were found between rural and urban settings (P = .02), with urban areas showing higher odds of changes to care plans (odds ratio, 4.889; 95% confidence interval, 1.27-18.78). Key findings highlighted the persistence of postcrisis documentation changes and varied definitions of surge criteria based on organizational leadership, policy, and mandates. The study yielded insights for modifying documentation, offering policy recommendations, and emphasizing ongoing collaboration and evidence-based approaches for future nursing practices. 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. As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths. As the awareness on violent deaths from guns, drugs, and suicides emerges as a public health crisis in the United States, attempts to prevent injury and mortality through nursing research are critical. The National Violent Death Reporting System provides public health surveillance of US violent deaths; however, understanding the National Violent Death Reporting System's research utility is limited. The purpose of our rapid review of the 2019-2023 literature was to understand to what extent artificial intelligence methods are being used with the National Violent Death Reporting System. We identified 16 National Violent Death Reporting System artificial intelligence studies, with more than half published after 2020. The text-rich content of National Violent Death Reporting System enabled researchers to center their artificial intelligence approaches mostly on natural language processing (50%) or natural language processing and machine learning (37%). Significant heterogeneity in approaches, techniques, and processes was noted across the studies, with critical methods information often lacking. The aims and focus of National Violent Death Reporting System studies were homogeneous and mostly examined suicide among nurses and older adults. Our findings suggested that artificial intelligence is a promising approach to the National Violent Death Reporting System data with significant untapped potential in its use. Artificial intelligence may prove to be a powerful tool enabling nursing scholars and practitioners to reduce the number of preventable, violent deaths. 
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