Cin-Computers Informatics Nursing最新文献

{"title":"The Relationship Between Anxiety and Readiness Levels Regarding Artificial Intelligence in Midwives: An Intergenerational Comparative Study.","authors":"Ayşe Nur Yilmaz, Sümeyye Altiparmak, Remziye Sökmen","doi":"10.1097/CIN.0000000000001269","DOIUrl":"10.1097/CIN.0000000000001269","url":null,"abstract":"<p><p>This study aimed to compare Generations X, Y, and Z in terms of anxiety and readiness levels regarding artificial intelligence and investigate the relationship between anxiety and readiness levels regarding artificial intelligence in midwives across generations. This study is cross-sectional and comparative with a study sample of 218 midwives working in a province in the east of Turkey. Data were collected with the \"Personal Information Form,\" \"Artificial Intelligence Anxiety Scale,\" and \"Medical Artificial Intelligence Readiness Scale.\" The evaluation of the data was carried out using the IBM SPSS Statistics version 22.0 (IBM Inc., Armonk, NY, USA) package program. Descriptive statistics, a one-way analysis of variance test, Pearson correlation, and regression analysis were used to analyze the data. The total mean score of midwives from the Artificial Intelligence Anxiety Scale was 47.07 ± 12.10 in Generation X, 43.91 ± 12.63 in Generation Y, and 36.16 ± 12.61 in Generation Z ( P < .05), and the difference between the groups was significant. Generation X had a higher level of artificial intelligence anxiety than Generation Y, and Generation Y had higher levels of artificial intelligence than Generation Z. The total mean score of midwives from the Medical Artificial Intelligence Readiness Scale was 67.43 ± 14.28 in Generation X, 66.78 ± 17.83 in Generation Y, and 74.73 ± 16.15 in Generation Z ( P < .05), and the difference between the groups was significant. Generation Z is more ready for medical artificial intelligence than Generation X, and Generation X is more ready for medical artificial intelligence than Generation Y. In addition, in the regression analysis, there was a weakly negative and significant relationship between the mean scores of Artificial Intelligence Anxiety Scale and Medical Artificial Intelligence Readiness Scale in Generation Z midwives, and as artificial intelligence anxiety levels increased, medical artificial intelligence readiness levels decreased. The artificial intelligence anxiety levels of midwives differed by generation, being highest in Generation X and lowest in Generation Z, and the level of medical artificial intelligence readiness was highest in Generation Z and lowest in Generation Y. As artificial intelligence anxiety increased in Generation Z midwives, the level of medical artificial intelligence readiness decreased.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Immersive Virtual Reality Simulation Scenario to Improve Empathy in Nursing Students.","authors":"Rosemary Collier, Rosa Darling, Karen Browne","doi":"10.1097/CIN.0000000000001259","DOIUrl":"10.1097/CIN.0000000000001259","url":null,"abstract":"<p><p>Empathy is essential in nursing practice and can be taught throughout nursing curriculum using a variety of methods including clinical experiences, in-person simulation, virtual reality, and didactic lecture. Empathy can also change over time, often decreasing the longer nurses practice. A cohort of upper-level nursing students viewed a short immersive virtual reality simulation as part of routine curriculum and completed the Toronto Empathy Questionnaire before viewing (time 1), 2 weeks later (time 2), and, for a small cohort, several months later (time 3). The sample included 110 undergraduate nursing students. There were no improvements in Toronto Empathy Questionnaire scores from time 1 to time 2. There was no improvement from time 1 to time 3 for the cohort who completed the Toronto Empathy Questionnaire three times. There were no significant differences in Toronto Empathy Questionnaire scores between cohorts for any measurement times. Total mean empathy scores were comparatively high in this study and did not decline over time. Although this virtual reality simulation scenario appears to have protected against decline in empathy, it may have been insufficient to foster an increase in empathy scores. Empathic training needs to be immersed throughout their nursing education in both didactic and clinical settings.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consumer Access and Utilization of Patient Portals for Electronic Health Records: A Cross-Sectional Study in Saudi Arabia.","authors":"Raniah N Aldekhyyel, Norah Alshafi, Lina Almohsen, Tharaa Alhowaish, Lina Alabbad, Raseel Alwahibi, Dena Alsuhaibani, Reem Aldekhyyel, Sripriya Rajamani","doi":"10.1097/CIN.0000000000001244","DOIUrl":"10.1097/CIN.0000000000001244","url":null,"abstract":"","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors Influencing eHealth Literacy Related to Parenting Among Asian Immigrant Mothers in South Korea.","authors":"Hyunmi Son, Young-Sil Sohn, Jung-Hee Jeon","doi":"10.1097/CIN.0000000000001253","DOIUrl":"10.1097/CIN.0000000000001253","url":null,"abstract":"<p><p>Immigrants face barriers to accessing healthcare owing to language and cultural differences. Considering the eHealth literacy of immigrant mother is important, particularly as many rely on online resources for information on childcare. This observational cross-sectional study aimed to identify the factors influencing eHealth literacy among immigrant mothers with young children in South Korea. We hypothesized that factors influencing eHealth literacy include perceived ease of seeking, credibility, positive experiences, and subjective norms for online health information, as conceptualized by the Technology Acceptance Model, including cultural adaptation. The analysis results revealed that perceived ease of seeking ( β = .45), positive experiences ( β = .14), and subjective norms ( β = .15) significantly affected eHealth literacy. Additionally, integrated cultural adaptation ( β = .23) and the child's medical history ( β = .11) significantly influenced eHealth literacy. To enhance eHealth literacy related to parenting for immigrant mothers, educating them on search strategies for online health information and fostering positive user experiences are crucial. Furthermore, these interventions should adopt a family-focused approach, with integrated cultural adaptation proving more beneficial for effective settlement support of immigrant mothers.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Chronic Pain Nursing Diagnosis Through Machine Learning: A Performance Evaluation.","authors":"Davide Macrì, Nicola Ramacciati, Carmela Comito, Elisabetta Metlichin, Gian Domenico Giusti, Agostino Forestiero","doi":"10.1097/CIN.0000000000001277","DOIUrl":"10.1097/CIN.0000000000001277","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Immersive Virtual Reality Simulation Scenario to Improve Empathy in Nursing Students.","authors":"","doi":"10.1097/CIN.0000000000001321","DOIUrl":"10.1097/CIN.0000000000001321","url":null,"abstract":"","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":"43 5","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nurses' Experiences of Using Nursing Care Plans in the Electronic Medical Record in an Acute Medical Setting: A Mixed-Methods Study.","authors":"Rebecca Miriam Jedwab, Isabella McDonald, Bernice Redley, Naomi Dobroff, Alemayehu Mekonnen","doi":"10.1097/CIN.0000000000001316","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001316","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Suitability of Low-Severity Rating Hospital Incident Reports for Machine Learning.","authors":"Rebecca Miriam Jedwab, Leonard Hoon, Caroline Luu, Bernice Redley","doi":"10.1097/CIN.0000000000001249","DOIUrl":"10.1097/CIN.0000000000001249","url":null,"abstract":"<p><p>Electronic incident reporting is a key quality and a safety process for healthcare organizations that assists in evaluating performance and informing quality improvement initiatives. Although it is mandatory for high-severity incident reports to be investigated, the majority, classified as low severity, are seldom examined due to the large volume of reports, constraints of human cognitive capacity to process such large amounts of data, and the limited resources available in healthcare organizations. The purpose of this study was to investigate low-severity incident reports for suitability of future machine learning to identify actionable interventions for harm prevention. This qualitative descriptive study used a yearlong dataset of low incident severity rating reports to model the incident reporting documentation workflow and explored findings with five nursing and healthcare quality and safety experts. Incident severity reports were reported to have multiple conflicting issues including information duplication, subjective data, too many selection options, and absence of contextual information resulting in a lack of usefulness of information for machine learning. Next steps include analysis of a dataset for machine learning suitability. Recommendations include end-user involvement in system redesign to ensure hospital incident reports are comprised of meaningful data.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Virtual Reality Educational Content on Magnetic Resonance Imaging: A Pilot Study.","authors":"Hye Jin Yoo, Sang Min Kim","doi":"10.1097/CIN.0000000000001285","DOIUrl":"10.1097/CIN.0000000000001285","url":null,"abstract":"<p><p>Through virtual reality technology, users experience challenging situations in a virtual world without physical experiences. This study aims to develop educational content using virtual reality to help patients undergoing magnetic resonance imaging and evaluate its usability. This pilot study developed virtual reality educational content using the ADDIE (analysis, design, development, implementation, and evaluation) model. An educational needs assessment targeted 20 experts and patients at a tertiary hospital. The content developed included pre-magnetic resonance imaging nursing, the magnetic resonance imaging process, and post-magnetic resonance imaging nursing. In pre-magnetic resonance imaging nursing, patients completed consent forms and received preparation instructions. The magnetic resonance imaging process included the environment, vision, and noise experienced during the examination. Post-magnetic resonance imaging nursing included precautions. An additional 12 experts and patients subsequently participated in virtual reality implementation and evaluation. Virtual reality evaluation included survey and semistructured face-to-face individual interviews. It scored 96.5 points out of 100 in usability, with little difference between experts' and patients' evaluations. In the qualitative evaluation, virtual reality educational content was revealed to be a useful approach, and the final virtual reality educational content was completed by reflecting the improvements suggested by participants. The findings offer tangible benefits for both healthcare professionals and patients by addressing the challenges associated with magnetic resonance imaging procedures through innovative educational interventions using virtual reality technology. Virtual reality educational content can be used as a practical training method in clinical settings.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topics and Trends in Neonatal Family-Centered Care: A Text Network Analysis and Topic Modeling Approach.","authors":"In Young Cho, Cho Hee Kim","doi":"10.1097/CIN.0000000000001310","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001310","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144038007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}