{"title":"The Efficacy of Online Visually Enhanced Mental Simulation in Nursing Students' Management of Lethal Rhythms.","authors":"Eylem Topbaş, Songül Demir","doi":"10.1097/CIN.0000000000001461","DOIUrl":"10.1097/CIN.0000000000001461","url":null,"abstract":"<p><p>This study examined the efficacy of online visually enhanced mental simulation (VEMS) in nursing students' management of lethal rhythms. The study population consisted of third-year students enrolled in the intensive care nursing course at a university's nursing department. The sample included 31 students who volunteered to participate and met the inclusion criteria. First, students completed an online theoretical course on lethal rhythm management as part of their intensive care curriculum. One week after the theoretical training, they took a pre-test. Next, they participated in simulation training, which included debriefing and online VEMS sessions. Finally, 1 week after the simulation, students completed the post-test. The post-test scores showed a statistically significant improvement in students' abilities to recognize lethal rhythms and perform initial assessments. The findings indicate that online VEMS is an effective educational approach for improving nursing students' abilities to recognize lethal rhythms and perform initial assessments. Accordingly, incorporating online VEMS into the nursing curriculum is recommended as a cost-effective strategy to promote nontechnical skills in online learning environments.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yi Ming Lu, Shu Wei Zhang, Hui Jie Wang, Yan Zhang, Guo Zeng Zhang
{"title":"Internet-delivered Psychological Interventions for Patients with Postpartum Depression: A Scoping Review.","authors":"Yi Ming Lu, Shu Wei Zhang, Hui Jie Wang, Yan Zhang, Guo Zeng Zhang","doi":"10.1097/CIN.0000000000001547","DOIUrl":"10.1097/CIN.0000000000001547","url":null,"abstract":"<p><strong>Background: </strong>Postpartum depression can have severe negative impacts on both the mother and her infant. In recent years, internet-delivered psychological interventions have gradually been regarded as potential solutions owing to their convenience and high accessibility.</p><p><strong>Methods: </strong>A scoping review was conducted using Arksey and O'Malley's methodological framework, with an updated systematic search across 4 databases (PubMed, Web of Science, Embase, and the Cochrane Library) up to January 13, 2026.</p><p><strong>Results: </strong>Twenty studies met the inclusion criteria and assessed the efficacy of diverse internet-delivered psychological interventions for managing postpartum depression. Most intervention measures are implemented based on websites. When designing personalized psychological interventions, the following 6 strategies can be adopted: psychotherapy courses, professional involvement, virtual community, timed reminders, additional learning resources, lesson summaries, and action plans. In addition, the possible obstacles encountered during the implementation process were systematically identified, and potential improvement paths were further explored.</p><p><strong>Conclusion: </strong>Internet-delivered psychological interventions offer an efficient and flexible solution for patients with postpartum depression, effectively compensating for the limitations of traditional offline psychological interventions. However, some challenges remain to be addressed during implementation to further enhance the intervention's effectiveness and feasibility. In the future, more diverse and flexible strategies are needed to meet the needs of different patients.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148348662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Koen P M van den Braak, Jessica D Veldhuizen, Sigrid Wulfse-Huisman, Jeroen Bakker, Pel-Littel Ruth E, Bianca M Buurman, Mariëlle Zondervan-Zwijnenburg, Nienke Bleijenberg
{"title":"Developing a Dashboard to Stimulate Independent Functioning in Older Persons in District Nursing Care.","authors":"Koen P M van den Braak, Jessica D Veldhuizen, Sigrid Wulfse-Huisman, Jeroen Bakker, Pel-Littel Ruth E, Bianca M Buurman, Mariëlle Zondervan-Zwijnenburg, Nienke Bleijenberg","doi":"10.1097/CIN.0000000000001508","DOIUrl":"10.1097/CIN.0000000000001508","url":null,"abstract":"<p><p>The desire of older adults to remain in their homes despite health challenges, as well as the aging population and rising health care costs, has prompted a shift from institutional care to home-based care. This change places district nurses at the forefront of supporting older adults' independent functioning. However, limited evidence exists on effective interventions by district nurses and using routine patient care data to enhance independence. To address this, a user-centered design approach was applied to develop and validate a dashboard providing district nurses with actionable insights to improve care quality and promote independence. The process followed a 4-phase iterative framework: user and domain analysis through focus groups and interviews, data preparation with anonymized patient data sets, dashboard prototyping, and evaluation through feedback from nurses and IT specialists. Iterative end-user involvement with district nurses, nursing assistants, researchers, and managers ensured relevance and usability. The final prototype includes functionalities for monitoring changes over time and linking problems to practical aids and intervention targets. The dashboard, tailored for district nurses, provides a tool to monitor and improve the independent functioning of older adults. Future research will evaluate the dashboard's impact on nursing interventions and its effectiveness in enhancing independent functioning.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13641250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147990731","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}
Lin Chen, Xiuli Tang, Hongbiao Gao, Li Sun, Mi Han, Jing Liang, Yue Xu, Meng Zhang, Meiqing Chen, Ran Cheng, Xiaohui Xiang, Qian Zhao, Yuehe Ma, Ling Liu, Jia Xu
{"title":"ChatGPT in the Development and Evaluation of Patient Satisfaction and Experience Questionnaires in Emergency Departments: A Feasibility Study.","authors":"Lin Chen, Xiuli Tang, Hongbiao Gao, Li Sun, Mi Han, Jing Liang, Yue Xu, Meng Zhang, Meiqing Chen, Ran Cheng, Xiaohui Xiang, Qian Zhao, Yuehe Ma, Ling Liu, Jia Xu","doi":"10.1097/CIN.0000000000001630","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001630","url":null,"abstract":"<p><p>Patient satisfaction and experience surveys are essential for assessing the quality of emergency nursing care, yet conventional questionnaire development is often slow because it relies on multiple rounds of expert consensus, translation, linguistic editing, and pilot testing. To explore whether large language models can streamline this process, we evaluated the feasibility of integrating ChatGPT into the design and preliminary validation of an emergency-department (ED) patient satisfaction and experience questionnaire. A three-phase methodological feasibility study was conducted in 2 tertiary EDs between March and August 2025. In Phase I, ChatGPT-4 and ChatGPT-5 (OpenAI, San Francisco, CA) generated 40 candidate items across 5 service domains. In Phase II, a 10-member Delphi panel rated relevance and clarity and we calculated item- and scale-level content validity indices (I-CVI and S-CVI). In Phase III, 200 ED patients completed both the ChatGPT-generated questionnaire and a nurse-developed comparison tool; reliability (Cronbach's α), item-total correlations, readability, and completion time were compared. ChatGPT produced well-structured items covering waiting time, communication, empathy, environment, professionalism, and perceived safety. The ChatGPT questionnaire achieved an S-CVI of 0.91 (vs. 0.93), with higher internal consistency (α=0.92 vs. 0.89), better readability (Flesch-Kincaid grade 6.8 vs. 8.1; P<.01), and a 32% shorter completion time. EFA and CFA further supported stronger structural validity for the AI-generated instrument than for the nurse-developed comparator. These findings support ChatGPT, particularly GPT-5.0, as a practical aid for rapid, reliable early-stage instrument development in nursing research.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149077345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Telehealth Use in Asian Americans: Closing or Widening the Gap in Health Access?","authors":"Chanam Shin, Thomas Guffey, Fuqin Liu","doi":"10.1097/CIN.0000000000001613","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001613","url":null,"abstract":"<p><p>Telehealth use rapidly expands and provides critical access to health care services. However, evidence is scarce regarding telehealth use in Asian Americans. We conducted a cross-sectional study in 265 Asian American adults and examined personal, interpersonal, clinical, access, and geographic factors that interplay in telehealth use, using data from the 2022 Health Information National Trends Survey. Data analysis used descriptive statistics and logistic regressions. Telehealth was used by 42.4% of Asian Americans during the last year. Asian American males and those with low income were less likely to use telehealth, whereas those in the West region, diagnosed with depression, and with better patient-centered communication were more likely to use telehealth. Among them, a diagnosis of depression was the most significant predictor of telehealth use (odds ratio=4.66, 95% CI=1.45-14.97). Telehealth was well accepted by Asian Americans; however, significant disparities existed in telehealth use by personal, interpersonal, clinical, access, and geographic factors. Communication that is both clear and patient-centered is essential for telehealth to bridge the gap in health access. Adequate training is also essential for health care providers to maximize quality care and promote positive patient outcomes. Future research identifying and addressing specific predictors of telehealth to each Asian American ethnic subgroup is crucial to clearly portray disparities in telehealth use across subgroups. National health surveys need oversampling of Asian Americans for a direct comparison with other racial groups and within Asian subgroups.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149032939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sammy A Kimanzi, Lucy W Kivuti-Bitok, Dorcas W Maina, Eunice A Omondi
{"title":"Artificial Intelligence-Driven Care Plans: Enhancing Nursing Care Efficiency in Kenya's National Referral Hospital Critical Care Unit: Quasi-Experimental Study.","authors":"Sammy A Kimanzi, Lucy W Kivuti-Bitok, Dorcas W Maina, Eunice A Omondi","doi":"10.1097/CIN.0000000000001628","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001628","url":null,"abstract":"<p><p>The application of artificial intelligence (AI) in nursing care has surfaced as a revolutionary method for improving the quality and efficiency of patient care. Examining AI-driven care plans could yield valuable insights for improving nursing care. A pretest-posttest quasi-experimental study with 43 participants compared manual and AI-generated care plans in performance expectancy, effort expectancy, user satisfaction, and efficiency, before and after the intervention. AI-driven care plans were produced through www.careplans.com in line with NANDA taxonomy. Participants were selected consecutively and completed a self-administered questionnaire. Data analysis was conducted using R (v4.1.2), utilizing the Wilcoxon signed-rank test for paired data. Results were illustrated in box plots, with differences deemed significant at P-values <.05. Care plans generated by AI consistently demonstrated superior performance compared to those developed manually across all evaluated criteria. The median scores for AI-generated plans were 91.67%, 93.75%, 91.67%, and 92.86% in perceived efficiency. In contrast, the manually written plans achieved scores of 58.33%, 56.25%, 50%, and 45%, respectively. Wilcoxon signed-rank test indicated that these differences were statistically significant (P< .001). Utilising efficient AI-generated care plans enhances nursing care, allowing for the reallocation of time saved to focus on direct patient care.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reliability and Quality of AI-Generated Nursing Care Plans: A 2-Year Follow-Up Comparing GPT-3.5 and GPT-5.","authors":"Funda Çam, Ayşe Dost, Mahmut Dağcl","doi":"10.1097/CIN.0000000000001627","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001627","url":null,"abstract":"<p><p>Advancements in large language models have increased the use of AI-generated content in nursing education; however, concerns remain regarding the reliability and quality of such outputs. This study aimed to evaluate the reliability and quality of nursing care plans generated by ChatGPT-5 and to compare them with those previously produced using ChatGPT-3.5. This methodological follow-up study used a descriptive and comparative design. Forty nursing diagnoses previously analyzed in 2023 were re-evaluated using ChatGPT-5 to ensure methodological continuity. Generated care plans were assessed using a descriptive criteria form and the DISCERN instrument. Paired comparisons were conducted using the Wilcoxon signed-rank test. ChatGPT-5-generated care plans showed significantly higher numbers of total and accessible references and higher DISCERN reliability and total scores than those reported in the 2023 evaluation of ChatGPT-3.5. No significant differences were observed in care-related information quality or overall quality scores; however, category-based analyses showed a shift toward higher quality levels. ChatGPT-5-generated nursing care plans showed higher reference accessibility and reliability scores than those reported in the 2023 evaluation of ChatGPT-3.5 using the same nursing diagnoses, although human oversight remains essential, particularly regarding reference accuracy.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149019534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Theresa M Stujenske, Ruth M Foreman, David A Nolfi
{"title":"A Scoping Review of Menstrual Cycle Technologies for Ovulation Tracking.","authors":"Theresa M Stujenske, Ruth M Foreman, David A Nolfi","doi":"10.1097/CIN.0000000000001624","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001624","url":null,"abstract":"<p><strong>Background: </strong>Recent developments in menstrual cycle tracking technologies that record physiological biomarkers of ovulation allow women to conveniently track their menstrual health data. The rapid pace of development of these technologies has made it challenging for health care professionals to evaluate their clinical validity.</p><p><strong>Methods: </strong>The purpose of this scoping review was to synthesize the current evidence regarding menstrual cycle tracking technologies that record physiological biomarkers of ovulation. The PRISMA extension for scoping reviews (PRISMA-ScR) was used to guide this review. Embase, PubMed, and Scopus databases were searched for publications regarding menstrual cycle tracking technologies that record physiological biomarkers of ovulation. The authors performed a thorough search of the literature using several keywords and database subject headings. Inclusion criteria were primary research articles available in English, assessed humans, and assessed menstrual cycle tracking technologies available to US consumers.</p><p><strong>Results: </strong>Title and abstract screening of all potentially relevant populations (n=2845) resulted in a full-text review of 139 publications. Twenty-three publications met the inclusion criteria, and 16 menstrual cycle tracking technologies that record physiological biomarkers of ovulation were identified, including temperature devices, urine hormone devices, and others.</p><p><strong>Discussion: </strong>The existing evidence for menstrual cycle tracking technologies relies on observational study designs and lacks diverse study populations, limiting the broader clinical applicability of these technologies. With further research, these technologies hold significant potential to improve menstrual health monitoring.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149019552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Emergency Nurses' Experiences of Digital Silence: A Qualitative Study.","authors":"Betül Bal, Nurşen Kulakaç","doi":"10.1097/CIN.0000000000001621","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001621","url":null,"abstract":"<p><p>This study aimed to explore emergency department nurses' perceptions and experiences of digital silence. A qualitative study was conducted with 14 emergency department nurses from two public hospitals in Turkey. Data were collected through semistructured in-person interviews and analyzed using the Framework Method for qualitative data analysis. Four overarching themes were identified: (i) awareness of digital silence, (ii) types of digital silence, (iii) causes of digital silence, and (iv) strategies for prevention. Digital silence manifests in several forms, including failure or partial completion of data entry, the use of alternative communication channels in place of official digital systems, and disregard for system alarms or alerts. The reasons underlying digital silence stem from both individual factors and organizational factors. Strategies to reduce digital silence involved training, development of user-friendly systems, establishment of nonpunitive reporting environments, and workload management. The results revealed that digital silence extends beyond individual attitudes, representing a multidimensional phenomenon closely associated with institutional structures, organizational culture, and the design features of digital systems. Enhancing system usability, reinforcing technical infrastructure, and promoting digital literacy were identified as key strategies for reducing digital silence behaviors.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149019529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}