JMIR nursing最新文献

{"title":"Training Programs for Dementia Care Staff and Aggression in Nursing Homes: Secondary Analysis of a Cross-Sectional Online Survey.","authors":"Reed Bratches, Tamara Backhouse, Shaoqing Ge, Megan Hebdon, Dalton Riegel, Noah Stein, Katie Trainum, Rita Jablonski","doi":"10.2196/94077","DOIUrl":"10.2196/94077","url":null,"abstract":"<p><strong>Background: </strong>Training programs help dementia care staff handle residents' behavioral symptoms in nursing homes and assisted living facilities. However, it is unclear how feeling well-prepared from such training relates to experiences of physical and verbal aggression from residents.</p><p><strong>Objective: </strong>This study aimed to determine whether perceived training preparedness was related to experiences of physical and verbal aggression from residents in nursing homes and assisted living facilities using data from a large, nationally representative dementia care workforce sample.</p><p><strong>Methods: </strong>This study was an analysis of the 2024 National Dementia Workforce Study, a cross-sectional online survey of the national dementia care workforce. Weighted regression modeling was used to identify associations between feeling well-prepared by training programs and experiencing physical or verbal aggression.</p><p><strong>Results: </strong>Dementia care staff in nursing homes (weighted n=896,320) commonly reported experiencing physical (365,452/896,320, 40.8%) and verbal (470,536/896,320, 52.5%) aggression from residents with dementia, whereas more than two-thirds (614,561/896,320, 68.6%) reported feeling that their training prepared them well to manage resident behaviors. Staff in assisted living facilities (n=738,856) less commonly reported experiencing physical (198,964/738,856, 26.9%) and verbal (223,343/738,856, 30.2%) aggression, whereas almost three-quarters (527,621/738,856, 71.4%) reported feeling that their training prepared them well to manage resident behaviors. In regression modeling in nursing homes, feeling well-prepared by training for managing resident behaviors was associated with a lower likelihood of experiencing physical aggression (odds ratio [OR] 0.4, 95% CI 0.223-0.711; P=.01) and verbal aggression (OR 0.5, 95% CI 0.299-0.844; P=.02). In assisted living facilities, feeling well-prepared by training for managing resident behaviors was associated with a lower likelihood of experiencing physical (OR 0.14, 95% CI 0.04-0.55; P=.01) and verbal (OR 0.07, 95% CI 0.0134-0.354; P=.001) aggression.</p><p><strong>Conclusions: </strong>While physical and verbal aggression from residents with dementia is common in nursing homes and assisted living facilities, nationally representative weighted regression modeling highlights an association between feeling well-prepared to handle resident behaviors and a lower likelihood of experiencing aggression. As the data used were derived from a cross-sectional survey, the causal link between these 2 factors should be explored in interventional studies. Our results indicate that effective training programs are associated with worker-reported reduced experiences of physical and verbal aggression in both nursing homes and assisted living facilities. The specific components of training programs that may support effectiveness remain unknown.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e94077"},"PeriodicalIF":5.0,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13353908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148427068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Shift From Efficacy to Implementation Science (2020-2026) in Nursing Practice for Digital Mental Health: Scoping Review and Bibliometric Analysis.","authors":"Shoukai Yu, Lingmei Qian, Hao Wang","doi":"10.2196/91498","DOIUrl":"10.2196/91498","url":null,"abstract":"<p><strong>Background: </strong>Nurses are pivotal as end users and implementers of digital mental health interventions (DMHIs). However, the successful translation of efficacious DMHIs into sustainable nursing practice is hindered by multifaceted implementation challenges.</p><p><strong>Objective: </strong>This study aimed to systematically map and analyze the evolving research landscape of nurse-involved DMHIs to determine if a paradigm shift from efficacy testing to implementation science is occurring and characterize the methodological and thematic trends associated with this shift.</p><p><strong>Methods: </strong>We conducted a scoping review integrated with a bibliometric analysis following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive search of the Web of Science Core Collection, Scopus, and PubMed databases (January 2020 to April 2026) yielded 1014 eligible primary research studies. Studies were classified as \"efficacy/effectiveness\" or \"implementation\" research using a validated framework. We analyzed temporal trends and methodological designs and performed co-word thematic mapping.</p><p><strong>Results: </strong>Implementation research constituted the largest proportion of clearly classified studies (327/470, 69.6%), exceeding efficacy research (143/470, 30.4%). Implementation research demonstrated a significant growth trajectory from 2020 to 2025 (β=8.63 articles per year; P=.003), surpassing efficacy research in 2022. Implementation studies used more mixed methods (105/327, 32.1% vs 7/143, 4.9%; P<.001) and qualitative designs (92/327, 28.1% vs 2/143, 1.4%; P<.001). Thematic analysis revealed \"nursing workflow integration\" and \"nurse-led implementation\" as emerging motor themes.</p><p><strong>Conclusions: </strong>This review provides robust evidence of an accelerating turn to implementation in digital mental health research in nursing. The field is moving beyond internal validity toward real-world implementation. Future efforts should focus on implementation-ready digital tools and embedding implementation science competencies into nursing education.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e91498"},"PeriodicalIF":5.0,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medication-Related Errors Among Nurses by Unit Adaptation Levels: Bayesian Network-Based Exploratory Study.","authors":"Naotaka Sugimura, Katsuhiko Ogasawara","doi":"10.2196/87436","DOIUrl":"10.2196/87436","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Medical errors occur more frequently in health care than in other industries due to challenges in patient safety education for nurses and students. To address this, it is important to identify the factors and structures underlying clinical errors and apply these insights to educational programs. Medication-related errors are highly preventable with appropriate interventions, highlighting the importance of data-driven safety education. Previous research suggests that unit adaptation, rather than clinical experience alone, plays a critical role in error occurrence. Focusing on \"adaptive performance,\" an underexplored concept in nursing, can help identify new educational strategies and interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This hypothesis-generating study used Bayesian network modeling to examine how unit experience-and, secondarily, total nursing experience-relates to the structure of medication-related errors, with implications for data-driven patient safety education and nursing student instruction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This mixed methods study conducted a qualitative root cause analysis of medication error reports to identify causal factors. Bayesian network modeling, an artificial intelligence-based approach, was used to visualize error-generation flows and compare models based on years of experience within the current unit. Data were obtained from 2023 medication-related error reports submitted by nurses to the Japan Council for Quality Health Care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among 119 medication-related incidents, the largest proportion occurred in internal medicine departments (n=47 cases, 39.5%), followed by surgical departments (n=27 cases, 22.7%), the intensive care unit (n=10 cases, 8.4%), and the emergency room (n=7 cases, 5.9%). Most incidents occurred in patient rooms or wards (n=97 cases, 81.5%). Root cause analysis identified 10 types of medication-related events and 23 contributing factors. Cases were categorized into low-, moderate-, and high-adaptation groups based on years of experience in the current unit. In the low-adaptation group (n=33), 69.7% of nurses had ≥5 years of total nursing experience, indicating that the group did not consist solely of novice nurses. The average entropy of incident events across the Bayesian network models ranged from 0.37 to 0.78, suggesting moderate to relatively high uncertainty in the inferred error structures. The moderate- and high-adaptation models exhibited fewer complex error networks, with weaker chains of unsafe conditions or actions than the low-adaptation model. The extensive clinical experience did not always prevent errors; rather, it was often linked to lapses in verification behavior.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Unit adaptation appears to play a critical role in shaping error pathways. Nurses with lower unit adaptation demonstrated more complex error structures, indicating higher vulnerability to incident chains. I","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e87436"},"PeriodicalIF":5.0,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13340574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WeChat-Based Nursing Interventions in Women's Mobile Health: Systematic Review.","authors":"Jun Xiao, Rui Li, Yangyang Wu, Tong Wu","doi":"10.2196/82679","DOIUrl":"10.2196/82679","url":null,"abstract":"<p><strong>Background: </strong>Mobile health (mHealth) technology offers new approaches to improve women's health by providing personalized monitoring and real-time guidance. As one of the most widely used social media platforms in China, WeChat has shown great potential in mHealth practice, yet systematic evidence on its application in women's health care remains insufficient.</p><p><strong>Objective: </strong>This study aims to systematically review WeChat-based nursing interventions in women's mHealth in order to clarify the application status, intervention modalities, target populations, and effectiveness outcomes.</p><p><strong>Methods: </strong>Searches were conducted in IEEE Xplore, Web of Science, PubMed, Scopus, ACM Digital Library, and Cochrane Central Register of Controlled Trials between January 2011 and December 2024. Two independent reviewers screened studies; extracted data on study design, intervention forms, target diseases, and outcome indicators; and assessed methodological quality. The Cohen κ coefficient was used to evaluate interreviewer agreement. Publication trends, institutional collaborations, author contributions, and research hotspots were analyzed using VOSviewer (Leiden University) and InCites (Clarivate) for bibliometric analysis.</p><p><strong>Results: </strong>A total of 31 eligible studies published from 2014 to 2024 were included. Most studies were randomized controlled trials (n=27). Intervention modalities mainly included WeChat groups (n=22), official accounts (n=18), applets (n=4), and private chats (n=9), mostly used in combination. The top focused health issues were prenatal care (n=5), breast cancer (n=5), gynecological cancer (n=5), and gestational diabetes mellitus (n=4). Six studies adopted multidisciplinary teams. Cohen κ was 0.71, indicating substantial agreement. Publications grew rapidly after 2018, peaking in 2021 and 2024. A total of 40 institutions participated, with Xi'an Jiaotong University having the highest citation impact. Most studies were at high risk of bias due to a nonblinding design.</p><p><strong>Conclusions: </strong>WeChat-based nursing interventions improve personalized health information access, self-management ability, treatment compliance, and real-time doctor-patient communication for women. This is the first systematic review to evaluate WeChat mHealth interventions in women's health, filling the research gap. Future research should focus on improving methodological quality, exploring cross-cultural adaptability, conducting long-term follow-up, and integrating wearable devices and electronic health records to further optimize WeChat-based women's health services.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e82679"},"PeriodicalIF":5.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13335764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148400244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.","authors":"Ben-Chang Shia, Szu-Ming Peng, Qui-Yang Zhang, Chiung-Yun Lo, Sheng-Ru Wang","doi":"10.2196/94450","DOIUrl":"10.2196/94450","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Inequitable and time-consuming shift scheduling contributes to nurse burnout, dissatisfaction, and turnover. In Taiwan, annual nurse turnover reaches 11.6%, with rigid 3-shift systems and unfair workload distribution frequently cited as key drivers. Although artificial intelligence (AI) scheduling tools exist, most lack transparency and do not formally address algorithmic bias, limiting clinical adoption.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to design, deploy, and evaluate a transparent, fairness-audited, explainable AI-enabled nurse scheduling decision support system (XAI-NSDSS) to reduce administrative burden, eliminate experience-based algorithmic bias, and enhance staff acceptance in a real-world hospital setting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A pragmatic before-after implementation study was conducted at a 671-bed teaching hospital in Taiwan (January-December 2023), involving 8 departments and 156 nurses (42 novice, 78 midlevel, and 36 experienced). A 6-month manual scheduling baseline (January-June 2023) was compared with a 6-month AI-assisted period (July-December 2023). The XAI-NSDSS integrates a random forest workload prediction model (R²=0.887), Shapley Additive Explanations-based explainability, a hybrid integer programming and binary differential evolution (IP+ BDE) optimizer, and a multidimensional fairness monitoring dashboard. A formal weight sensitivity analysis (WSA) was conducted across 7 prespecified weight configurations using full-factorial repeated-measures ANOVA to assess outcome robustness. Primary outcomes were scheduling time, error rate, and user satisfaction. Statistical analyses used linear mixed effects models (LMMs) and generalized estimating equations (GEE) with department as a random effect.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Monthly scheduling time decreased by 81.2% (mean 32.0, SD 8.0-mean 6.0, SD 2.0) hours; P&lt;.001; Cohen d=4.33) and error rate decreased by 73.8% (mean 18.3, SD 4.3%-mean 4.8, SD 1.2%; P&lt;.001; Cohen d=4.12). Nurse satisfaction improved from a mean of 3.2 (SD 0.8) to a mean of 4.4 (SD 0.6; P&lt;.001), with 148 out of 156 nurses (94.9%) adopting the system by Month 3. Preexisting experience-based bias was fully eliminated: workload coefficient of variation (CV) decreased 50% (0.18-0.09; P&lt;.001), disparate impact ratios normalized from 1.35-1.56 to 1.01-1.04, and preference satisfaction equity was achieved across experience tiers (ANOVA P=.38). Among 156 nurses, 82 (52.6%) regularly engaged with Shapley Additive Explanations; this engagement was positively associated with satisfaction (Pearson r=0.456; P&lt;.001). The WSA across 7 configurations confirmed that the consensus-derived default weights achieved the highest composite quality score (mean 82.1, SD 3.2) and that disparate impact ratios remained within the 0.80-1.25 fairness threshold across all configurations (P=.12), demonstrating structural robustness of the fairness-auditing module.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Co","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e94450"},"PeriodicalIF":5.0,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148378434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"360° Contextual Simulation Videos for Undergraduate Nursing Students: Electroencephalography-Based Quasi-Experimental Study.","authors":"Yi-Chang Chen, Chen-Jung Chen","doi":"10.2196/84720","DOIUrl":"10.2196/84720","url":null,"abstract":"<p><strong>Background: </strong>Screen-based 360° contextual simulation videos are increasingly being used in nursing education; however, evidence regarding their effects on learning outcomes and neural electroencephalography (EEG) patterns remains limited.</p><p><strong>Objective: </strong>This study aims to evaluate whether integrating 360° contextual simulation videos with traditional classroom instruction influences nursing students' perceptions of simulation design and educational practices, their levels of satisfaction and self-confidence in learning, and EEG-based neural correlates of action observation.</p><p><strong>Methods: </strong>In a quasi-experiment, 55 third-year nursing students (experimental n=28; control n=27) were assigned to 360° contextual simulation video learning (laptop; no head-mounted display) or traditional classroom-based instruction (didactic instruction, guided discussion, and 2D instructional videos). The primary outcomes were Simulation Design Scale (SDS) and Educational Practices Questionnaire (EPQ) scores. Secondary outcomes included Student Satisfaction and Self-Confidence in Learning Scale (SCLS) scores and EEG indices (power at C3, Cz, and C4; μ-rhythm suppression scores). Group differences were tested with 2-tailed t tests and analysis of covariance adjusted for baseline values.</p><p><strong>Results: </strong>Between-group analyses (analysis of covariance adjusted for baseline values) revealed higher posttest scores on the SDS (F1,35=7.80, η²=.182; P=.008), EPQ (F1,35=6.00, η²=.146; P=.02), and SCLS (F1,35=7.01, η²=.167; P=.01) in the experimental group compared to the control group. Within-group analyses (paired t tests) indicated significant increases in SDS and EPQ scores for the experimental group (P<.001 and P=.005), whereas the control group showed a decrease in the SDS score (P=.001) and no significant change in the EPQ score (P=.11). The SCLS score did not significantly change in the experimental group (P=.21) but increased in the control group (P=.04). EEG analyses revealed significant postexposure reductions in power at C3, Cz, and C4 for the experimental group (all P<.001), with between-group effects at Cz (F1,35=425.29, η²=.926; P<.001) and C4 (F1,35=5.45, η²=.138; P=.03). μ-Suppression, which is an indirect EEG correlate that is often reported during action observation, was greater in the experimental group at Cz (P=.03) and C4 (P=.03) but not at C3 (P=.13). No adverse events were reported.</p><p><strong>Conclusions: </strong>Integrating 360° contextual simulation videos into undergraduate nursing education was associated with higher SDS and EPQ scores and improved self-reported satisfaction and self-confidence, in addition to EEG-based findings that may reflect neural EEG patterns associated with action observation. Larger multisite randomized trials are warranted to confirm efficacy and cost-effectiveness.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e84720"},"PeriodicalIF":5.0,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13318206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nurses' Experiences Using AI in Clinical Practice: Systematic Review.","authors":"Ashley J S Scott, Qimeng Zhao, Jo-Fan Pan, Benjamin C Brown, Dawn Dowding","doi":"10.2196/91238","DOIUrl":"10.2196/91238","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) tools are increasingly used in clinical settings, yet most syntheses focus on nurses' attitudes or readiness rather than experiences after direct use in practice.</p><p><strong>Objective: </strong>This study aimed to synthesize registered nurses' experiences of using AI in clinical practice and to identify perceived benefits, barriers, and implementation implications.</p><p><strong>Methods: </strong>We conducted a systematic literature review of empirical studies reporting nurses' experiences of AI use in clinical settings. Searches were performed in CINAHL, Embase, MEDLINE, PsycINFO, and PubMed (last search: September 13, 2023). Moreover, 2 reviewers (AJSS and QZ) independently screened titles, abstracts, and full texts and appraised included studies using the Mixed Methods Appraisal Tool. We used thematic synthesis with a primarily deductive framework based on the Technology Acceptance Model 2, with the addition of facilitating conditions from the unified theory of acceptance and use of technology.</p><p><strong>Results: </strong>In total, 20 studies met the inclusion criteria. Perceived usefulness and facilitating conditions were most frequently reported; nurses described AI as supporting decision-making, workflow efficiency, and confidence when implementation included adequate training, interoperability, and technical infrastructure. Ease of use was closely tied to interface design and training. Job relevance and output quality were rated more positively when nurses described AI as aligning with nursing tasks and as producing interpretable, reliable outputs. Common barriers included usability issues, limited integration into workflows and electronic systems, privacy and trust concerns, and inconsistent or poorly contextualized outputs. Across studies, nurses often described adoption as conditional on organizational readiness and meaningful involvement of nurses in design and implementation.</p><p><strong>Conclusions: </strong>Nurses' accounts suggest that AI may augment clinical work, but the perceived benefits reported in the included literature were contingent on workflow alignment, usable interfaces, training, and supportive infrastructure. These findings suggest potential value in involving nurses in the co-design and iterative refinement of AI tools, grounded in the consistent evidence that facilitating conditions, such as training, interoperability, and organizational readiness, are the primary determinants of whether AI is experienced positively. Prospective evaluation using nursing-relevant outcomes, such as usability, workflow integration, and trust, is needed to move beyond post hoc experiential accounts and establish what implementation conditions reliably produce benefit.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e91238"},"PeriodicalIF":5.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13296498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148320971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Associations of Emotional Intelligence, AI Self-Efficacy, and AI Literacy Among Nursing Undergraduates Under the NUR.S.E.S. Framework: Network Analysis.","authors":"XiaoHui Fan, Jingjing Sun, LingHui Zhang, Jinrong Wang, BinHao Dong, XiaoHong Gao, Ruihua Jin, Panpan Huai","doi":"10.2196/90253","DOIUrl":"10.2196/90253","url":null,"abstract":"<p><strong>Background: </strong>With the rapid development of generative artificial intelligence (AI) and its deep integration into nursing education, nursing students' AI literacy (AS) has become a critical competency for their professional development. However, the patterns of associations among emotional intelligence (EI), AI self-efficacy (AILS), and AS in relation to comprehensive AS remain unclear.</p><p><strong>Objective: </strong>Based on the NUR.S.E.S. framework and using network analysis methods, this study systematically mapped the complex relational network among EI, AILS, and AS among undergraduate nursing students. It identified nodes with high centrality and bridging strength within this network, offering preliminary insights that may inform future educational interventions.</p><p><strong>Methods: </strong>A cross-sectional survey design was used, with 982 undergraduate nursing students from a university conveniently sampled in September 2025 as research participants. Assessments were conducted using the EI Scale, the AILS Scale, and the AS Scale. Using R (version 4.5.1; R Core Team), we constructed a Gaussian graph model, calculated centrality metrics such as node and bridge strength, and assessed network stability using the bootstrap method.</p><p><strong>Results: </strong>Network analysis showed that emotion regulation (strength centrality=1.355) and evaluative ability (strength centrality=1.323) showed the highest strength centrality, indicating their prominent positions within the network. Emotional perception (bridge strength=0.427) and comfort with AI (bridge strength=0.242) are the most critical bridge nodes, appearing to connect EI with AI technology systems. Simultaneously, the network architecture suggests that AILS may play a bridging role, effectively linking EI (particularly emotional perception as a bridging factor) with higher levels of AS.</p><p><strong>Conclusions: </strong>Cultivating AS among undergraduate nursing students is a system that deeply integrates emotional, cognitive, and technical confidence. EI was closely associated with AS, and AILS appeared to occupy a bridging position in the network. Educational interventions might consider enhancing emotional perception and comfort with AI, pending validation through longitudinal or experimental designs.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e90253"},"PeriodicalIF":5.0,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13292984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148321032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Health Literacy and Attitudes Toward Telehealth Use in Practice Among Nursing Students in Saudi Arabia: Cross-Sectional Study.","authors":"Muna Alharbi, Ahmed S Alharthi, Abdulrahman A Azab, Renad Alharthi","doi":"10.2196/94722","DOIUrl":"10.2196/94722","url":null,"abstract":"<p><strong>Background: </strong>Nursing students are the future workforce, and their readiness to use digital health tools is important. Previous studies have focused on knowledge and attitudes; however, they have not examined the wide range of digital health literacy levels that may influence nursing students' attitudes toward using telehealth in clinical settings.</p><p><strong>Objective: </strong>This study aimed to determine the relationship between nursing students' digital health literacy and their attitudes toward telehealth use in practice.</p><p><strong>Methods: </strong>A cross-sectional design was used. The sample consisted of undergraduate nursing students enrolled in a Bachelor of Nursing program at a selected Saudi Arabian university. The online survey used 2 scales: the Digital Health Care Literacy Scale and the Nurses' Attitudes Toward Use of a Telehealth Scale.</p><p><strong>Results: </strong>A total of 273 students participated (mean age 21.3, SD 1.9 years). Most of the nursing students demonstrated a high digital health literacy level (n=184, 67.4%; mean Digital Health Care Literacy Scale score 11.9 out of 15, SD 3.1). Digital health literacy was a significant predictor of positive attitudes toward telehealth use in practice (adjusted odds ratio 1.48, 95% CI 1.28-1.71; P<.001). Male students were significantly less likely to report positive attitudes than female students (adjusted odds ratio 0.62, 95% CI 0.39-0.97; P=.03). However, academic year, telehealth workshops, and informatics courses were not significantly associated with positive attitudes toward telehealth use in practice.</p><p><strong>Conclusions: </strong>Higher levels of literacy appear to correlate with more positive attitudes toward telehealth use in practice. However, current formal education and workshops had no apparent influence on digital health literacy. This suggests a potential need for strengthening digital training and development in nursing education. This may enhance telehealth readiness and support future digital health care delivery.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e94722"},"PeriodicalIF":5.0,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13290104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148310731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Leadership Scale for Clinical Nurses: Development and Validation of an Instrument.","authors":"Ji-Hyeon Lee, Sun-Young Jung","doi":"10.2196/82101","DOIUrl":"10.2196/82101","url":null,"abstract":"<p><strong>Background: </strong>The rapid advancement of digital technologies, combined with the evolving complexity of health care environments, has introduced a new paradigm in nursing practice. Clinical nurses are now required not only to deliver safe and effective patient care but also to demonstrate competencies in digital literacy and innovation. Among these emerging competencies, digital leadership has become a critical attribute-enabling nurses to lead digital transformation, ensure patient safety, enhance care quality, and support system-level change within health care organizations. Despite its increasing relevance, there is a notable absence of validated measurement tools tailored to assess digital leadership in clinical practice.</p><p><strong>Objective: </strong>This study aimed to develop and psychometrically validate a Digital Leadership Scale for Clinical Nurses (DLS-CN) to systematically evaluate the digital leadership capabilities of nurses working in clinical settings.</p><p><strong>Methods: </strong>The scale development process followed a rigorous multistep procedure. Initial items were derived from previous qualitative research involving a literature review and in-depth interviews, complemented by an additional literature review conducted in this study. The content validity of 38 preliminary items was evaluated by 9 experts over 2 rounds. A pilot test was conducted with 30 nurses, followed by cognitive interviews with 5 nurses to refine item clarity and relevance. The final set of items was administered to 446 clinical nurses across various health care institutions. Data were randomly split for exploratory factor analysis and confirmatory factor analysis. Additional analyses were conducted to evaluate item discrimination, convergent validity, and internal consistency using IBM SPSS 25.0 and AMOS 23.0.</p><p><strong>Results: </strong>The finalized DLS-CN consists of 29 items grouped under four domains: (1) ability to use digital technology, (2) digital safety management, (3) digital collaboration mindset, and (4) organizational influence. These 4 factors explained 56.9% of the total variance. The scale showed strong internal consistency (Cronbach α=0.95). Convergent validity was demonstrated through strong positive correlations with the Nursing Informatics Competency Scale (Pearson correlation coefficient r=0.82; P<.001) and the Self-Leadership Scale (Pearson correlation coefficient r=0.83; P<.001).</p><p><strong>Conclusions: </strong>The DLS-CN is a valid and reliable instrument for measuring digital leadership among clinical nurses. It offers a practical tool for educators, administrators, and researchers to assess and enhance digital leadership capabilities-ultimately supporting the digital transformation of health care systems.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e82101"},"PeriodicalIF":5.0,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13286069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148304219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}