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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. Nursing care plans within electronic medical record systems have the potential to support nurses in planning and prioritizing patient care; however, there is a gap in the literature related to nurses' experiences of how this may occur. The aims of this mixed-methods study included exploring nurses' documentation adherence, identifying barriers and enablers to care plans documentation, and making recommendations to enhance nurses' use of care plans within electronic medical records. An audit of 142 patients revealed the majority had at least one care plan initiated in the electronic medical record (n = 120, 84.5%), 63 patients had a care plan initiated within 24 hours of admission (n = 63, 44.4%), and only three had care plans documented against in the previous 48 hours (2.11%). Data from six focus groups were developed into two themes (each with two subthemes): “Mind the Gap” and “Making It Work for Us.” Barriers and enablers were identified and mapped to 10 of the 14 domains of the Theoretical Domains Framework. There was large variability in nurses' knowledge and understanding related to the need for care plans documentation. Assessment of usability and/or redesign of care plans within electronic medical records must align to nursing workflows to support clinical care delivery. 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. The majority of patients with cancer tend to seek health-related information via the Internet. This descriptive and cross-sectional study aims to determine e-health literacy, cyberchondria levels, and affecting factors in patients with cancer. The population of the study consisted of 113 patients who were older than 18 years, with no sensory loss that could hinder their communication, literate in Turkish language, who were conscious, actively used the Internet, and visited a university hospital's oncology and hematology polyclinic. In the multiple regression analysis examining the E-Health Literacy Scale total score according to sociodemographic and other characteristics, it was found that solitary complementary and alternative medicine explained 40.8% of the variance in the E-Health Literacy Scale score (adjusted R2 = 0.408, P 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. This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing diagnosis of chronic pain and explore the potential of artificial intelligence (AI) in enhancing clinical decision-making in Italian healthcare settings. Three machine learning algorithms—XGBoost, gradient boosting, and BERT—were optimized through a grid search approach to identify the most suitable hyperparameters for each model. Therefore, the performance of the algorithms was evaluated and compared using Cohen's κ coefficient. This statistical measure assesses the level of agreement between the predicted classifications and the actual data labels. Results demonstrated XGBoost's superior performance, whereas BERT showed potential in handling complex Italian language structures despite data volume and domain specificity limitations. The study highlights the importance of algorithm selection in clinical applications and the potential of machine learning in healthcare, specifically addressing the challenges of Italian medical language processing. This work contributes to the growing field of artificial intelligence in nursing, offering insights into the challenges and opportunities of implementing machine learning in Italian clinical practice. Future research could explore integrating multimodal data, combining text analysis with physiological signals and imaging data, to create more comprehensive and accurate chronic pain classification models tailored to the Italian healthcare system. Obesity is health epidemic associated with health conditions specific to women’s health. Healthcare providers must identify and develop a follow-up plan for patients with a body mass index of greater than 30 kg/m2 to meet the Merit-Based Incentive Payment System Quality Program rate for body mass index screening and follow-up. Barriers to addressing obesity in this population by healthcare providers include time available for counseling and knowledge about appropriate diagnosis and treatment options. This is a quality improvement project that implements a clinical template within an existing electronic health record platform that includes a treatment order set and prepopulated counseling prompts to improve the rate of which healthcare providers address obesity within the women’s health clinic. After 12 weeks, 27 patients started a weight management plan, and the Merit-Based Incentive Payment System rate increased from 59% to 67%. Implementation of order set templates into electronic health record platforms with counseling guidance provides a framework for providers to develop a plan to address obesity to meet their patient’s health goals and reduce health disparities related to obesity in women. Today's healthcare landscape is becoming increasingly data-centric, with artificial intelligence and advanced computer algorithms becoming inextricably embedded in patient care. Although these technologies promise to make care more efficient and effective, they heighten the risk for unintended consequences. Using Walker and Avant's framework for concept analysis, we propose and explicate the emerging concept of iatrogenic data trauma, or ways in which the collection, storage, and use of sensitive and potentially stigmatizing patient data can cause harm. We conducted a careful and exhaustive review of traditional academic publications, as well as nontraditional digital sources to generate a rich and intersectional corpus of information pertaining to data justice, digital rights, and potential risks associated with the “datafication” of individuals. Using evidence synthesis and practical examples, we discuss how flawed data processes in healthcare settings can lead to data trauma among patients and explore how its presence can perpetuate health disparities, marginalization, loss of privacy, and breach of trust in patient-provider relationships. We discuss how this phenomenon arises and manifests across the healthcare continuum and is an important issue for professionals in multiple disciplines. We conclude by suggesting future opportunities for research through a trauma-informed lens. This study aimed to use wearable technology to predict the sleep quality of family caregivers of people with dementia among underrepresented groups. Caregivers of people with dementia often experience high levels of stress and poor sleep, and those from underrepresented communities face additional burdens, such as language barriers and cultural adaptation challenges. Participants, consisting of 29 dementia caregivers from underrepresented populations, wore smartwatches that tracked various physiological and behavioral markers, including stress level, heart rate, steps taken, sleep duration and stages, and overall daily wellness. The study spanned 529 days and analyzed data using 70 features. Three machine learning algorithms—random forest, k nearest neighbor, and XGBoost classifiers—were developed for this purpose. The random forest classifier was shown to be the most effective, boasting an area under the curve of 0.86, an F1 score of 0.87, and a precision of 0.84. Key findings revealed that factors such as wake-up stress, wake-up heart rate, sedentary seconds, total distance traveled, and sleep duration significantly correlated with the caregivers' sleep quality. This research highlights the potential of wearable technology in assessing and predicting sleep quality, offering a pathway to creating targeted support measures for dementia caregivers from underserved groups. The study suggests that such technology can be instrumental in enhancing the well-being of these caregivers across diverse populations. Exploratory data analysis involves observing data in graphical formats before making any assumptions. If interesting relationships or patterns among variables are identified, hypotheses are developed for further testing. This study aimed to identify significant differences in the levels of exhaustion, resilience, sleep quality, and sleep hygiene according to the personal characteristics of middle-aged women transitioning into menopause or postmenopause through exploratory data analysis. A total of 200 women aged 44 to 55 years were recruited online in August 2023. Data were collected using valid instruments and analyzed through data visualization, pattern identification in the visualized data, and hypothesis establishment based on the visualized patterns. Hypotheses were tested through the independent-samples t test, analysis of variance, and the Kruskal-Wallis test. A total of 11 patterns and corresponding hypotheses were identified. According to the statistically supported pattern-based hypotheses, middle-aged women who were in their perimenopausal period perceived themselves as unhealthy, had professional occupations, and had the highest level of exhaustion and the lowest levels of resilience, sleep quality, and sleep hygiene. This study demonstrated that data visualization is an efficient way to explore relationships or patterns between data. Data visualization should be considered an informatics solution that can provide insight in the field of healthcare. Over the past several years, hospitals have utilized agency staffing to combat staffing shortages. Increased use of agency staffing presented an opportunity for implementation of an education project related to the potential variance in practice of permanent staffing, specifically with the use of infusion interoperability in the inpatient setting at the University of Pittsburgh Medical Center St Margaret hospital. Discussion around variables causing agency nurse setbacks with utilizing infusion interoperability while trying to meet the required standard laid the groundwork for this project. Improving agency workflows allowed for process improvement including enhanced quality, documentation, and adherence. Early data analysis revealed variance in adherence between agency and permanent staffing prompting further analysis. Investigational methods included assessment of agency nurse infusion interoperability usage through interviews and observations, review of adherence reports, review of education and onboarding, and interviewing of nurse leaders. Findings suggested lack of experience, inability to troubleshoot, and underutilized resources contributed to lower adherence with agency compared with permanent staff. These findings lead the informaticists to make changes to the curriculum for new hire onboarding, increase rounding and interactions with agency staff, and increase access to resources. These interventions resulted in increased adherence scores and verbalized satisfaction by the agency nurses. 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 
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