Computers, informatics, nursing : CIN最新文献_第6页

Exploration of Social Media Usage Among Nurses in the European Region: A Cross-Sectional Study. 欧洲地区护士社交媒体使用的探索：一项横断面研究。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-20 DOI: 10.1097/CIN.0000000000001616

Tiina Syyrilä, Laura Hako, Helena de Rezende, Ippolito Notarnicola, Marie-Louise Luiking, Jill Murphy, Helena Felizardo, Liz Westcott, Ilya Kagan

{"title":"Exploration of Social Media Usage Among Nurses in the European Region: A Cross-Sectional Study.","authors":"Tiina Syyrilä, Laura Hako, Helena de Rezende, Ippolito Notarnicola, Marie-Louise Luiking, Jill Murphy, Helena Felizardo, Liz Westcott, Ilya Kagan","doi":"10.1097/CIN.0000000000001616","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001616","url":null,"abstract":"The study aimed to explore nurses' use of social media for networking, cooperation, and learning purposes. This cross-sectional study collected data from 158 nurses using an online questionnaire, primarily distributed through open social media for Sigma nurses across 19 European countries in 2023. Over half of the respondents were Sigma nursing members [n=105 (66.4%)]. PhD or a second academic degree was held by 58.2% (n=92) of the respondents. The questionnaire examined (1) background characteristics; (2) social media platforms' use; (3) usage of social media for learning; and (4) networking. WhatsApp, Facebook, and Instagram were the most frequently used social media platforms. While most nurses did not perceive social media as a tool for learning, a significant portion reported using it for receiving, discussing, and posting knowledge, indicating a contradiction in perceptions of learning through social media. Social media was primarily used to stay updated on research, news, and webinars. Barriers to utilizing social media for learning addressed a lack of time, concerns over privacy, user skills, information overload, content reliability, and platform complexity. Social media can enhance learning and networking opportunities for nurses, but platforms must be selected and content tailored to meet their educational needs. Further research is necessary for generalizability, given the limited sample size and representativeness, and to optimize social media strategies for knowledge dissemination. This study adds to the literature by presenting indicative descriptive perspectives of predominantly highly educated nurses in the European region on social media use for professional networking, collaboration, and learning.","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815446","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}

引用次数: 0

Supporting Students to Identify a Research Project Topic: Harnessing a GenAI Chatbot to Transform a Repository of Former Student Projects. 支持学生确定研究项目主题：利用GenAI聊天机器人转换以前学生项目的存储库。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-19 DOI: 10.1097/CIN.0000000000001610

Huaqiong Zhou, Janie Brown, Brenton Lush, Tracey Moroney

{"title":"Supporting Students to Identify a Research Project Topic: Harnessing a GenAI Chatbot to Transform a Repository of Former Student Projects.","authors":"Huaqiong Zhou, Janie Brown, Brenton Lush, Tracey Moroney","doi":"10.1097/CIN.0000000000001610","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001610","url":null,"abstract":"Delays selecting a feasible research topic cause students anxiety and distress. Technologies like ChatGPT pose challenges to higher education delivery and assessment, but also create opportunities to address individual learning needs. This study was to develop, implement, and evaluate a GenAI chatbot to support students to identify a research topic. A 4-stage mixed-methods study guided by the active digital learning pedagogy: collation and synthesis of 252 former students' project topics, followed by development, implementation, and evaluation of a GenAI topic-bank chatbot. An online survey including the 11-item Bot Usability Scale and a 9-item purposefully developed survey based on active digital learning pedagogy was used to collect students' perceptions of chatbot usability and learning experiences. Students' overall learning experiences were explored using 2 open-ended questions. All 23 students, enrolled into the capstone project unit of semester 1 2025, utilized the chatbot, across 35 conversations and 233 searches. Each student initiated 1 to 3 conversations and 3 to 23 searches. Average bot usability scale score was 3.57/5 and learning evaluation was 3.95/5. Narratively, students shared mixed experiences and perceptions. The chatbot generally facilitated students in identifying project topics and improved their learning experience. Built on customized knowledge, the chatbot has potential application to other postgraduate courses' students.","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148805162","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}

引用次数: 0

Validity and Reliability of ICD Symptom Coding and Its Implications for Symptom Assessment and Research Using Electronic Health Records. ICD症状编码的效度和信度及其对使用电子健康记录进行症状评估和研究的意义。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-17 DOI: 10.1097/CIN.0000000000001609

Tru Cao, Meagan Whisenant, Hongmei Wang, Veronica Brady, David Lee, Ziqi Liu, Jingru Zhou, Yuxuan Gu, Ann Maliackal, Stephanie Merem, Hulin Wu

{"title":"Validity and Reliability of ICD Symptom Coding and Its Implications for Symptom Assessment and Research Using Electronic Health Records.","authors":"Tru Cao, Meagan Whisenant, Hongmei Wang, Veronica Brady, David Lee, Ziqi Liu, Jingru Zhou, Yuxuan Gu, Ann Maliackal, Stephanie Merem, Hulin Wu","doi":"10.1097/CIN.0000000000001609","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001609","url":null,"abstract":"Symptom data is critical to the diagnostic process, but little is known about how well symptoms are coded in electronic health records. Our study aimed to quantify the accuracy and completeness of symptom coding by ICD-9 and ICD-10. We randomly selected 150 unique patient-admission records from MIMIC-IV version 2.2. Four clinical experts reviewed the clinical notes in those records to extract and map the noted symptoms to 65 predefined symptom categories. In addition, the ICD diagnosis codes in those records were automatically extracted and mapped to 1888 previously identified ICD symptom codes with their predefined categories as applicable. We then computed the performance measures of ICD symptom coding compared with the clinical expert-confirmed symptoms. The obtained sensitivity, specificity, positive predictive value, and negative predictive value with the 95% confidence intervals were, respectively, 0.134 [0.117, 0.153], 0.998 [0.996, 0.999], 0.909 [0.862, 0.941], and 0.872 [0.865, 0.879]. To our knowledge, this study is the first attempt to quantitatively validate ICD symptom coding that is not limited to any specific diseases. The low sensitivity highlights the need to improve symptom coding. In addition, clinical notes remain essential for symptom extraction in automated symptom assessment and secondary data analyses using electronic health records.","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148805212","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}

引用次数: 0

Digital Health Management and Risk-Stratified Nursing for Pregnant Women With Gestational Diabetes Mellitus and Hypertensive Disorders of Pregnancy. 妊娠期糖尿病和妊娠期高血压疾病孕妇的数字化健康管理和风险分层护理

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-03 DOI: 10.1097/CIN.0000000000001595

Yangyan Wei, Yilin Song, Rongping Zhai, Xunyu Wu

{"title":"Digital Health Management and Risk-Stratified Nursing for Pregnant Women With Gestational Diabetes Mellitus and Hypertensive Disorders of Pregnancy.","authors":"Yangyan Wei, Yilin Song, Rongping Zhai, Xunyu Wu","doi":"10.1097/CIN.0000000000001595","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001595","url":null,"abstract":"We aimed to evaluate the intervention effect of risk-stratified nursing mode and digital health management on pregnant women with gestational diabetes mellitus (GDM) and hypertensive disorders of pregnancy (HDP). A prospective randomized controlled trial was conducted to recruit 120 GDM patients with HDP. A control group (n=60) and an intervention group (n=60) were established for random allocation. Both groups received conventional nursing, and the intervention group additionally received risk-stratified nursing by a mobile health platform throughout the third trimester of pregnancy. The intervention group had significantly lower preterm delivery rate, macrosomia incidence rate, and fetal distress incidence rate than those of the control group (P<.05). The cesarean section rate and maternal complication rate significantly decreased in the intervention group in contrast to those in the control group (P<.05). Before delivery, the intervention group outperformed the control group in the levels of fasting blood glucose, postprandial blood glucose (PBG), systolic blood pressure, and diastolic blood pressure (P<.05). The risk-stratified nursing mode plus digital health management can effectively ameliorate the blood glucose and blood pressure control in pregnant women with GDM and HDP.","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148671941","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}

引用次数: 0

Exploring Public Perception of Palliative Care and Hospice: Sentiment Analysis and Topic Modeling of YouTube Comments. 探索公众对姑息治疗和临终关怀的看法：YouTube评论的情绪分析和话题建模。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-01 DOI: 10.1097/CIN.0000000000001477

Hana Lee, Kyungmi Woo

{"title":"Exploring Public Perception of Palliative Care and Hospice: Sentiment Analysis and Topic Modeling of YouTube Comments.","authors":"Hana Lee, Kyungmi Woo","doi":"10.1097/CIN.0000000000001477","DOIUrl":"10.1097/CIN.0000000000001477","url":null,"abstract":"This study explores public perceptions of palliative and hospice care through analysis of user comments on Korean-language YouTube videos. A total of 12,155 comments were collected from 54 videos retrieved using the keywords \"palliative care\" and \"hospice.\" The search was conducted between September 11 and October 31, 2024. Sentiment analysis using a Korean-language sentiment lexicon revealed that the majority of comments were neutral (57.8%), followed by negative (23.0%) and positive (19.2%) sentiments. Topic modeling using the Latent Dirichlet Allocation method identified 13 recurring themes: religious beliefs, diagnosis and treatments, pain control, family caregiving, death with dignity, emotional farewell, social support and policy, life reflection, meaning of end-of-life care, hope, appreciation, decision-making, and remembrance and mourning. Nurses play an important role in addressing the public misconceptions and emotional concerns regarding end-of-life care that are frequently expressed on online platforms. Insights from this study can support the development of tailored communication strategies, promote early advance care planning, and inform informatics-based interventions to improve equitable access to hospice and palliative care. These findings also highlight the potential of social media analysis as a tool for understanding public needs and guiding data-informed nursing practice.","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147292049","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}

引用次数: 0

Risk Factors for Postoperative Delirium in Nonintensive Care Unit Patients: Machine Learning Approach. 非重症监护病房患者术后谵妄的危险因素：机器学习方法。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-01 DOI: 10.1097/CIN.0000000000001463

Hyungbok Lee, Taesa Ahn, Sohyeon Park, Mikyung Kim, Soyoon Park, Chaewon Oh, Suyeon Lee

引用次数: 0

Testing Machine Learning-Based Pain Assessment for Postoperative Geriatric Patients: Erratum. 基于机器学习的老年患者术后疼痛评估测试：勘误。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-01 DOI: 10.1097/CIN.0000000000001546

引用次数: 0

Digital Transformation in Ghana's Health Care Sector: Opportunities for Nursing in an Era of a National eHealth Strategy. 加纳卫生保健部门的数字化转型：国家电子卫生战略时代护理的机遇。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-01 DOI: 10.1097/CIN.0000000000001382

Wahab Osman, Archanaa Krisnagopal, Charlene H Chu

引用次数: 0

Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review. 预测乳腺癌放射性皮炎的机器学习模型：范围综述。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-01 DOI: 10.1097/CIN.0000000000001484

Jayana Castelo Branco Cavalcante de Meneses, Abilio Torres Dos Santos Neto, Maria Aparecida Ferreira Domingos, Pedro Luis Saraiva Barbosa, Régia Christina Moura Barbosa Castro, Gilmara Holanda da Cunha, Judith Sixsmith, Ana Fátima Carvalho Fernandes

{"title":"Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review.","authors":"Jayana Castelo Branco Cavalcante de Meneses, Abilio Torres Dos Santos Neto, Maria Aparecida Ferreira Domingos, Pedro Luis Saraiva Barbosa, Régia Christina Moura Barbosa Castro, Gilmara Holanda da Cunha, Judith Sixsmith, Ana Fátima Carvalho Fernandes","doi":"10.1097/CIN.0000000000001484","DOIUrl":"10.1097/CIN.0000000000001484","url":null,"abstract":"Background: Artificial intelligence, particularly machine learning, has great potential to improve health outcomes, including predicting adverse conditions. In breast cancer, machine learning models can help personalize prevention strategies for radiation-induced cutaneous toxicity.Methods: This scoping review aimed to explore machine learning models for predicting radiation dermatitis in women with breast cancer. Data collection was conducted in November 2023 from 7 electronic databases and gray literature, with no restrictions on publication year. Publication selection was supported by the RAYYAN reference manager, and ResearchRabbit software expanded the search.Results: A total of 22 publications were included. The reviewed models primarily predicted acute radiation dermatitis using clinical predictors. Most studies used cross-validation, and class imbalance was observed. The predominant models were developed using the Random Forest algorithm, with the Bayesian Network emerging as the top-performing model, incorporating clinical, clinicopathological, demographic, radiomic, and dosimetric predictors.Conclusion: This review underscores the importance of further investigation into multiomic biomarkers and the establishment of minimum nursing databases to support predictive model development for radiation dermatitis in breast cancer patients.","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146088805","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}

引用次数: 0

Collaboration, Intellectual Basis, and Emerging Trends in Smart Senior Care Research: A Scientometric Analysis. 智能老年护理研究中的合作、智力基础和新兴趋势：科学计量学分析。

IF 1.9

Computers, informatics, nursing : CIN Pub Date : 2026-08-01 DOI: 10.1097/CIN.0000000000001588

引用次数: 0