Pub Date : 2025-11-01DOI: 10.1016/j.ijnss.2025.08.003
Amina M. Ahmad , Wegdan Bani-Issa , Fatma Refaat , Muna S. Al-Tamimi , Taliaa M. Al-Yafeai
Objectives
The study aimed to examine the severity of moral distress and intention to leave among ICU nurses in the United Arab Emirates (UAE), and explore the influencing factors of intention to leave.
Methods
The study utilized a cross-sectional research design. A convenience non-probability sample of 341 nurses from various private and government hospitals across different emirates in the UAE participated in June 2022. Data were collected using a self-administered questionnaire comprising demographic information, the Moral Distress Scale–Revised. Multivariable logistic regression was used to identify factors associated with intention to leave.
Results
The study found that a large majority (71.9 %) of ICU nurses experienced severe moral distress, and more than 35 % had intention to leave. Futile end-of-life interventions emerged as the most distress-provoking aspect of practice [16.0 (0, 16.0)]. Multivariable analysis revealed nurses experiencing severe moral distress had 3.73 times the odds of intending to leave their job compared with those experiencing mild distress (95 %CI: 1.81, 7.69; P < 0.001) and being aged 31–40 years (OR = 2.02; 95 %CI: 1.23, 3.33; P = 0.005) was independently associated with a higher intention to leave.
Conclusions
Severe moral distress was prevalent among ICU nurses in the UAE and strongly associated with intention to leave, and also those aged 31–40 years. Promoting ethical support, shared decision-making, and nurse empowerment is vital to improving retention and care quality.
目的调查阿联酋ICU护士道德困扰程度和离职意向，探讨离职意向的影响因素。方法采用横断面研究设计。2022年6月，来自阿联酋不同酋长国的各种私立和政府医院的341名护士参加了一项便利非概率样本。数据收集使用自我管理问卷，包括人口统计信息，道德困扰量表-修订。使用多变量逻辑回归来确定与离职意图相关的因素。结果研究发现，绝大多数（71.9%）ICU护士存在严重的道德困境，超过35%的护士有离职意向。无效的临终干预成为实践中最令人痛苦的方面[16.0(0,16.0)]。多变量分析显示，经历严重道德困扰的护士离职的几率是经历轻度道德困扰的护士的3.73倍（95% CI: 1.81, 7.69; P < 0.001）， 31-40岁（OR = 2.02; 95% CI: 1.23, 3.33; P = 0.005）与更高的离职意愿独立相关。结论阿联酋ICU护士普遍存在严重的道德困扰，且与离职意向密切相关，31-40岁的ICU护士也是如此。促进道德支持、共同决策和护士赋权对于提高留用率和护理质量至关重要。
{"title":"Moral distress and intention to leave among intensive care unit nurses in the United Arab Emirates","authors":"Amina M. Ahmad , Wegdan Bani-Issa , Fatma Refaat , Muna S. Al-Tamimi , Taliaa M. Al-Yafeai","doi":"10.1016/j.ijnss.2025.08.003","DOIUrl":"10.1016/j.ijnss.2025.08.003","url":null,"abstract":"<div><h3>Objectives</h3><div>The study aimed to examine the severity of moral distress and intention to leave among ICU nurses in the United Arab Emirates (UAE), and explore the influencing factors of intention to leave.</div></div><div><h3>Methods</h3><div>The study utilized a cross-sectional research design. A convenience non-probability sample of 341 nurses from various private and government hospitals across different emirates in the UAE participated in June 2022. Data were collected using a self-administered questionnaire comprising demographic information, the Moral Distress Scale–Revised. Multivariable logistic regression was used to identify factors associated with intention to leave.</div></div><div><h3>Results</h3><div>The study found that a large majority (71.9 %) of ICU nurses experienced severe moral distress, and more than 35 % had intention to leave. Futile end-of-life interventions emerged as the most distress-provoking aspect of practice [16.0 (0, 16.0)]. Multivariable analysis revealed nurses experiencing severe moral distress had 3.73 times the odds of intending to leave their job compared with those experiencing mild distress (95 %<em>CI</em>: 1.81, 7.69; <em>P</em> < 0.001) and being aged 31–40 years (<em>OR</em> = 2.02; 95 %<em>CI</em>: 1.23, 3.33; <em>P</em> = 0.005) was independently associated with a higher intention to leave.</div></div><div><h3>Conclusions</h3><div>Severe moral distress was prevalent among ICU nurses in the UAE and strongly associated with intention to leave, and also those aged 31–40 years. Promoting ethical support, shared decision-making, and nurse empowerment is vital to improving retention and care quality.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 581-587"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This integrative review aimed to examine and synthesize existing empirical evidence on nurse-led clinics (NLCs) in primary care settings, with a focus on models of care implemented globally.
Methods
The review adhered to PRISMA guidelines, with rigorous inclusion and exclusion criteria applied. A systematic search was conducted across the Cochrane Library, EMBASE, Medline via EBSCO, PubMed, ScienceDirect, Scopus, and bibliographic databases for studies published between 2014 and 2024. Eligible studies included original, peer-reviewed research focused on nurse-led or nurse-managed clinics. A convergent integrated synthesis approach and thematic analysis were employed to identify key models of care.
Results
The search yielded 1,651 records; 13 studies met the inclusion criteria. Data synthesis revealed six distinct models of care implemented in community-based nurse-led clinics: Innovative Cognitive Care, Integrated Multidisciplinary Care, Community-Driven Underserved Population Care, Reproductive and Women's Health Innovation, Palliative Care Model, and Behavioral Health Integration.
Conclusions
Nurse-led models of care are crucial for strengthening primary healthcare, particularly in underserved settings. Further research and policy support are needed to expand nurses' roles, enhance their competencies, and promote interdisciplinary collaboration for the delivery of sustainable and equitable health services.
{"title":"Enhancing accessibility through nurse-led clinics in primary care: An integrative review of models of care","authors":"Yajai Sitthimongkol, Manassawee Srimoragot, Weha Kasemsuk, Saovaros Meekusol, Pokkrong Pongpattanapisit, Pennapa Saenkla, Suebsarn Ruksakulpiwat","doi":"10.1016/j.ijnss.2025.10.006","DOIUrl":"10.1016/j.ijnss.2025.10.006","url":null,"abstract":"<div><h3>Objectives</h3><div>This integrative review aimed to examine and synthesize existing empirical evidence on nurse-led clinics (NLCs) in primary care settings, with a focus on models of care implemented globally.</div></div><div><h3>Methods</h3><div>The review adhered to PRISMA guidelines, with rigorous inclusion and exclusion criteria applied. A systematic search was conducted across the Cochrane Library, EMBASE, Medline via EBSCO, PubMed, ScienceDirect, Scopus, and bibliographic databases for studies published between 2014 and 2024. Eligible studies included original, peer-reviewed research focused on nurse-led or nurse-managed clinics. A convergent integrated synthesis approach and thematic analysis were employed to identify key models of care.</div></div><div><h3>Results</h3><div>The search yielded 1,651 records; 13 studies met the inclusion criteria. Data synthesis revealed six distinct models of care implemented in community-based nurse-led clinics: Innovative Cognitive Care, Integrated Multidisciplinary Care, Community-Driven Underserved Population Care, Reproductive and Women's Health Innovation, Palliative Care Model, and Behavioral Health Integration.</div></div><div><h3>Conclusions</h3><div>Nurse-led models of care are crucial for strengthening primary healthcare, particularly in underserved settings. Further research and policy support are needed to expand nurses' roles, enhance their competencies, and promote interdisciplinary collaboration for the delivery of sustainable and equitable health services.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 593-600"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ijnss.2025.10.013
Kamila Rosamilia Kantovitz , Maria Eduarda Mattoso , Maria Davoli Meyer , Marcella Armbruster de Araújo , Priscila Alves Giovani , Valentin Martinez , Nileshkumar Dubey , Francisco Humberto Nociti Jr , Rogério Heládio Lopes Motta
Objectives
Breastfeeding plays a critical role in the healthy development of infants, yet exclusive breastfeeding (EBF) rates remain low, particularly among low-income mothers. This study aimed to develop and validate an AI-based educational innovative solution to increase breastfeeding literacy across caregivers and mothers.
Methods
The BabyChat (AI-based) was developed through two phases. In phase I, the content was created using the Canvas application, with the idea tree structured through MindMeister, and delivered via the ManyChat tool on Facebook. The focus was on the benefits of EBF during the initial 6 months of life, as recommended by the WHO, and continued breastfeeding until 1,000 days of life. In Phase II, functionality tests were performed using UserTesting and subsequently validated by the Content Validity Index (CVI). Healthcare professionals reviewed the clarity and relevance of the information on a four-point scale. Intra-examiner concordance was assessed by percentage of agreement and the median for each CVI-I point.
Results
The contents of BabyChat included 8 topics and 18 subtopics (based on relevant contents including nutritional and anatomical aspects, weaning strategies among others) aimed to educate mothers and caregivers. Five mothers participated in evaluation of the BabyChat. Overall, most participants found the chatbot’s question-and-answer functionality clear and helpful, with accurate command execution and timely response speeds, etc. However, two participants noted occasional issues such as misinterpreted questions, delayed command responses, and unclear or hard-to-find interface buttons. A total of four experts in psychology, dentistry, and medicine validated the framework. The agreement rate between experts ranged from 25 % to 100 %, with median values between 3 and 4, indicating excellent content relevance.
Conclusion
The BabyChat was developed and validated for use in increasing breastfeeding literacy among caregivers and mothers. Future studies should be considered to expand the BabyChat validation to other healthcare professionals, including nursing staff, to comprehensively capture the impact of BabyChat on mothers, as well as to incorporate population-specific topics that depend on cultural and geographical aspects.
{"title":"Increasing breastfeeding literacy: A preliminary study to develop an AI-based chatbot","authors":"Kamila Rosamilia Kantovitz , Maria Eduarda Mattoso , Maria Davoli Meyer , Marcella Armbruster de Araújo , Priscila Alves Giovani , Valentin Martinez , Nileshkumar Dubey , Francisco Humberto Nociti Jr , Rogério Heládio Lopes Motta","doi":"10.1016/j.ijnss.2025.10.013","DOIUrl":"10.1016/j.ijnss.2025.10.013","url":null,"abstract":"<div><h3>Objectives</h3><div>Breastfeeding plays a critical role in the healthy development of infants, yet exclusive breastfeeding (EBF) rates remain low, particularly among low-income mothers. This study aimed to develop and validate an AI-based educational innovative solution to increase breastfeeding literacy across caregivers and mothers.</div></div><div><h3>Methods</h3><div>The BabyChat (AI-based) was developed through two phases. In phase I, the content was created using the Canvas application, with the idea tree structured through MindMeister, and delivered via the ManyChat tool on Facebook. The focus was on the benefits of EBF during the initial 6 months of life, as recommended by the WHO, and continued breastfeeding until 1,000 days of life. In Phase II, functionality tests were performed using UserTesting and subsequently validated by the Content Validity Index (CVI). Healthcare professionals reviewed the clarity and relevance of the information on a four-point scale. Intra-examiner concordance was assessed by percentage of agreement and the median for each CVI-I point.</div></div><div><h3>Results</h3><div>The contents of BabyChat included 8 topics and 18 subtopics (based on relevant contents including nutritional and anatomical aspects, weaning strategies among others) aimed to educate mothers and caregivers. Five mothers participated in evaluation of the BabyChat. Overall, most participants found the chatbot’s question-and-answer functionality clear and helpful, with accurate command execution and timely response speeds, etc. However, two participants noted occasional issues such as misinterpreted questions, delayed command responses, and unclear or hard-to-find interface buttons. A total of four experts in psychology, dentistry, and medicine validated the framework. The agreement rate between experts ranged from 25 % to 100 %, with median values between 3 and 4, indicating excellent content relevance.</div></div><div><h3>Conclusion</h3><div>The BabyChat was developed and validated for use in increasing breastfeeding literacy among caregivers and mothers. Future studies should be considered to expand the BabyChat validation to other healthcare professionals, including nursing staff, to comprehensively capture the impact of BabyChat on mothers, as well as to incorporate population-specific topics that depend on cultural and geographical aspects.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 509-515"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ijnss.2025.10.001
Li Jiang , Qianjun Xia , Min Yang
Objectives
To assess the methodological quality of recent clinical practice guidelines (CPGs) and consensus statements on premature ovarian insufficiency (POI) to formulate a nurse-oriented management framework, thus promoting nurses’ adherence and advancing evidence-based nursing practice.
Methods
A systematic search was conducted to identify CPGs and consensus statements on POI published in English or Chinese between 2019 and 2024. The methodological quality of included CPGs was independently assessed by two authors using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument. Similarly, the quality of consensus statements was evaluated using the Joanna Briggs Institute (JBI) Checklist for Text and Opinion papers. Recommendations from high-quality publications were extracted and synthesized into a preliminary management framework. This framework was specifically tailored to align with the perspective and clinical context of nursing practice. The preliminary framework was subsequently refined through an expert consultation process to ensure its validity and practicality.
Results
Four CPGs and two consensus statements, all rated as “high quality”, were included in the framework. Concordance between the authors ranged from substantial to near-perfect agreement (0.79–1.0). In developing the framework, recommendations from the CPGs were identified and consolidated into three categories: management of high-risk POI populations, management of POI patients, and management of patients with POI-related complications.
Conclusions
The included CPGs and consensus statements concerning POI were all recommended for use in clinical practice. Using existing evidence, we developed a nurse-oriented management framework to bolster nurses’ adherence to the guidelines and foster evidence-based nursing practices. Further research is needed to provide evidence-based health care in this field.
{"title":"Toward a nurse-oriented management framework for premature ovarian insufficiency: Integration of guidelines and consensus recommendations","authors":"Li Jiang , Qianjun Xia , Min Yang","doi":"10.1016/j.ijnss.2025.10.001","DOIUrl":"10.1016/j.ijnss.2025.10.001","url":null,"abstract":"<div><h3>Objectives</h3><div>To assess the methodological quality of recent clinical practice guidelines (CPGs) and consensus statements on premature ovarian insufficiency (POI) to formulate a nurse-oriented management framework, thus promoting nurses’ adherence and advancing evidence-based nursing practice.</div></div><div><h3>Methods</h3><div>A systematic search was conducted to identify CPGs and consensus statements on POI published in English or Chinese between 2019 and 2024. The methodological quality of included CPGs was independently assessed by two authors using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument. Similarly, the quality of consensus statements was evaluated using the Joanna Briggs Institute (JBI) Checklist for Text and Opinion papers. Recommendations from high-quality publications were extracted and synthesized into a preliminary management framework. This framework was specifically tailored to align with the perspective and clinical context of nursing practice. The preliminary framework was subsequently refined through an expert consultation process to ensure its validity and practicality.</div></div><div><h3>Results</h3><div>Four CPGs and two consensus statements, all rated as “high quality”, were included in the framework. Concordance between the authors ranged from substantial to near-perfect agreement (0.79–1.0). In developing the framework, recommendations from the CPGs were identified and consolidated into three categories: management of high-risk POI populations, management of POI patients, and management of patients with POI-related complications.</div></div><div><h3>Conclusions</h3><div>The included CPGs and consensus statements concerning POI were all recommended for use in clinical practice. Using existing evidence, we developed a nurse-oriented management framework to bolster nurses’ adherence to the guidelines and foster evidence-based nursing practices. Further research is needed to provide evidence-based health care in this field.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 573-580"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This prospective cohort study examined the change trajectories of cognitive and physical functions of individuals with motoric cognitive risk (MCR) syndrome, as well as the longitudinal associations between MCR syndrome and changes in cognitive and physical functions, to provide a new perspective on preventing dementia.
Methods
Participants were selected from the China Health and Retirement Longitudinal Study (CHARLS). Demographic characteristics, health status, and lifestyle variables were assessed in 2011. MCR syndrome was defined as the presence of subjective cognitive complaints and objective slow gait, with preserved activities of daily living and absence of dementia, and assessed in 2011. Cognitive function, including orientation, attention and calculation, episodic memory, and visuospatial ability, was measured from 2011 to 2018. Physical function, including grip strength, balance ability, and repeated chair stand tests, was measured from 2011 to 2015. Generalized estimating equation was employed to analyze the longitudinal associations between MCR syndrome in 2011 and changes in cognitive functions over 7 years and physical functions over 4 years.
Results
Among 4,217 participants, 475 had MCR syndrome in 2011. Both participants with MCR syndrome and those without exhibited a decline in both cognitive and physical function over 7 years and 4 years of follow-up, except for fluctuations in visuospatial ability. Non-MCR syndrome participants demonstrated significantly better overall cognitive function in 2018 compared to 2011 (Group × Time: B = 0.44, P = 0.035) than those in the MCR syndrome group. However, participants with non-MCR syndrome demonstrated significantly worse visuospatial ability in 2013 (Group × time: B = −0.44, P = 0.002) and 2018 (Group × time: B = −0.34, P = 0.016) compared to those in the MCR syndrome group. Non-MCR syndrome participants demonstrated significantly better performance in repeated chair stand tests in 2013 (Group × time: B = 0.31, P < 0.001) and 2015 (Group × time: B = 0.37, P < 0.001) compared to those in the MCR syndrome group in 2011.
Conclusions
Older adults with MCR syndrome experience worse overall cognitive and physical function performance, especially in repeated chair stand tests, than individuals without MCR syndrome over 7-year and 4-year follow-up periods. It is suggested that future interventional studies will target both physical and cognitive functions in MCR syndrome individuals, providing insights for the prevention of dementia.
目的本前瞻性队列研究探讨运动认知风险综合征（MCR）患者认知和身体功能的变化轨迹，以及MCR综合征与认知和身体功能变化的纵向关联，为预防痴呆提供新的视角。方法选择中国健康与退休纵向研究（CHARLS）的研究对象。2011年对人口特征、健康状况和生活方式变量进行了评估。MCR综合征被定义为存在主观认知抱怨和客观步态缓慢，保留日常生活活动和无痴呆，并于2011年进行评估。认知功能，包括定向、注意力和计算、情景记忆和视觉空间能力，从2011年到2018年进行了测量。身体功能，包括握力、平衡能力和重复的椅子支架测试，从2011年到2015年进行了测量。采用广义估计方程分析2011年MCR综合征与7年以上认知功能和4年以上身体功能变化的纵向关联。结果在4217名参与者中，2011年有475人患有MCR综合征。在7年和4年的随访中，除了视觉空间能力的波动外，患有MCR综合征和没有MCR综合征的参与者在认知和身体功能方面都表现出下降。与2011年相比，2018年非MCR综合征参与者的整体认知功能明显优于MCR综合征组（组×时间：B = 0.44, P = 0.035）。然而，与MCR综合征组相比，非MCR综合征参与者在2013年（x时间组：B = - 0.44, P = 0.002）和2018年（x时间组：B = - 0.34, P = 0.016）表现出明显较差的视觉空间能力。与2011年的MCR综合征组相比，非MCR综合征参与者在2013年（组x时间：B = 0.31, P < 0.001）和2015年（组x时间：B = 0.37, P < 0.001）的重复椅站测试中表现出明显更好的表现。结论在7年和4年的随访期间，患有MCR综合征的成年患者的整体认知和身体功能表现较差，尤其是在重复椅架测试中。建议未来的干预研究将针对MCR综合征个体的身体和认知功能，为预防痴呆提供见解。
{"title":"Cognitive and physical functions among Chinese community-dwelling older adults with motoric cognitive risk syndrome: A prospective cohort study","authors":"Junhong Wu, Xinyu Yao, Xing Wu, Yamei Bai, Yayi Zhao","doi":"10.1016/j.ijnss.2025.10.003","DOIUrl":"10.1016/j.ijnss.2025.10.003","url":null,"abstract":"<div><h3>Objectives</h3><div>This prospective cohort study examined the change trajectories of cognitive and physical functions of individuals with motoric cognitive risk (MCR) syndrome, as well as the longitudinal associations between MCR syndrome and changes in cognitive and physical functions, to provide a new perspective on preventing dementia.</div></div><div><h3>Methods</h3><div>Participants were selected from the China Health and Retirement Longitudinal Study (CHARLS). Demographic characteristics, health status, and lifestyle variables were assessed in 2011. MCR syndrome was defined as the presence of subjective cognitive complaints and objective slow gait, with preserved activities of daily living and absence of dementia, and assessed in 2011. Cognitive function, including orientation, attention and calculation, episodic memory, and visuospatial ability, was measured from 2011 to 2018. Physical function, including grip strength, balance ability, and repeated chair stand tests, was measured from 2011 to 2015. Generalized estimating equation was employed to analyze the longitudinal associations between MCR syndrome in 2011 and changes in cognitive functions over 7 years and physical functions over 4 years.</div></div><div><h3>Results</h3><div>Among 4,217 participants, 475 had MCR syndrome in 2011. Both participants with MCR syndrome and those without exhibited a decline in both cognitive and physical function over 7 years and 4 years of follow-up, except for fluctuations in visuospatial ability. Non-MCR syndrome participants demonstrated significantly better overall cognitive function in 2018 compared to 2011 (Group × Time: <em>B</em> = 0.44, <em>P</em> = 0.035) than those in the MCR syndrome group. However, participants with non-MCR syndrome demonstrated significantly worse visuospatial ability in 2013 (Group × time: <em>B</em> = −0.44, <em>P</em> = 0.002) and 2018 (Group × time: <em>B</em> = −0.34, <em>P</em> = 0.016) compared to those in the MCR syndrome group. Non-MCR syndrome participants demonstrated significantly better performance in repeated chair stand tests in 2013 (Group × time: <em>B</em> = 0.31, <em>P</em> < 0.001) and 2015 (Group × time: <em>B</em> = 0.37, <em>P</em> < 0.001) compared to those in the MCR syndrome group in 2011.</div></div><div><h3>Conclusions</h3><div>Older adults with MCR syndrome experience worse overall cognitive and physical function performance, especially in repeated chair stand tests, than individuals without MCR syndrome over 7-year and 4-year follow-up periods. It is suggested that future interventional studies will target both physical and cognitive functions in MCR syndrome individuals, providing insights for the prevention of dementia.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 551-557"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ijnss.2025.10.011
Yunxia Duan , Rui Wang , Yumei Sun , Wendi Zhu , Yi Li , Na Yu , Yu Zhu , Peng Shen , Hongyu Sun
Objectives
This study aimed to develop and validate a stroke risk prediction model based on machine learning (ML) and regional healthcare big data, and determine whether it may improve the prediction performance compared with the conventional Logistic Regression (LR) model.
Methods
This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou (CHERRY) (2015–2021). We included adults aged 18–75 from the platform who had established records before 2015. Individuals with pre-existing stroke, key data absence, or excessive missingness (>30 %) were excluded. Data on demographic, clinical measures, lifestyle factors, comorbidities, and family history of stroke were collected. Variable selection was performed in two stages: an initial screening via univariate analysis, followed by a prioritization of variables based on clinical relevance and actionability, with a focus on those that are modifiable. Stroke prediction models were developed using LR and four ML algorithms: Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Back Propagation Neural Network (BPNN). The dataset was split 7:3 for training and validation sets. Performance was assessed using receiver operating characteristic (ROC) curves, calibration, and confusion matrices, and the cutoff value was determined by Youden’s index to classify risk groups.
Results
The study cohort comprised 92,172 participants with 436 incident stroke cases (incidence rate: 474/100,000 person-years). Ultimately, 13 predictor variables were included. RF achieved the highest accuracy (0.935), precision (0.923), sensitivity (recall: 0.947), and F1 score (0.935). Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR, with training/validation area under the curve (AUC)s of 0.777/0.779 (LR), 0.921/0.918 (BPNN), 0.988/0.980 (RF), 0.980/0.955 (DT), and 0.962/0.958 (XGBoost). Calibration analysis revealed a better fit for DT, LR and BPNN compared to RF and XGBoost model. Based on the optimal performance of the RF model, the ranking of factors in descending order of importance was: hypertension, age, diabetes, systolic blood pressure, waist, high-density lipoprotein Cholesterol, fasting blood glucose, physical activity, BMI, low-density lipoprotein cholesterol, total cholesterol, dietary habits, and family history of stroke. Using Youden’s index as the optimal cutoff, the RF model stratified individuals into high-risk (>0.789) and low-risk (≤0.789) groups with robust discrimination.
Conclusions
The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model, providing an effective tool for risk stratification in primary stroke prevention in community settings.
{"title":"Development and validation of a stroke risk prediction model using regional healthcare big data and machine learning","authors":"Yunxia Duan , Rui Wang , Yumei Sun , Wendi Zhu , Yi Li , Na Yu , Yu Zhu , Peng Shen , Hongyu Sun","doi":"10.1016/j.ijnss.2025.10.011","DOIUrl":"10.1016/j.ijnss.2025.10.011","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to develop and validate a stroke risk prediction model based on machine learning (ML) and regional healthcare big data, and determine whether it may improve the prediction performance compared with the conventional Logistic Regression (LR) model.</div></div><div><h3>Methods</h3><div>This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou (CHERRY) (2015–2021). We included adults aged 18–75 from the platform who had established records before 2015. Individuals with pre-existing stroke, key data absence, or excessive missingness (>30 %) were excluded. Data on demographic, clinical measures, lifestyle factors, comorbidities, and family history of stroke were collected. Variable selection was performed in two stages: an initial screening via univariate analysis, followed by a prioritization of variables based on clinical relevance and actionability, with a focus on those that are modifiable. Stroke prediction models were developed using LR and four ML algorithms: Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Back Propagation Neural Network (BPNN). The dataset was split 7:3 for training and validation sets. Performance was assessed using receiver operating characteristic (ROC) curves, calibration, and confusion matrices, and the cutoff value was determined by Youden’s index to classify risk groups.</div></div><div><h3>Results</h3><div>The study cohort comprised 92,172 participants with 436 incident stroke cases (incidence rate: 474/100,000 person-years). Ultimately, 13 predictor variables were included. RF achieved the highest accuracy (0.935), precision (0.923), sensitivity (recall: 0.947), and F1 score (0.935). Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR, with training/validation area under the curve (AUC)s of 0.777/0.779 (LR), 0.921/0.918 (BPNN), 0.988/0.980 (RF), 0.980/0.955 (DT), and 0.962/0.958 (XGBoost). Calibration analysis revealed a better fit for DT, LR and BPNN compared to RF and XGBoost model. Based on the optimal performance of the RF model, the ranking of factors in descending order of importance was: hypertension, age, diabetes, systolic blood pressure, waist, high-density lipoprotein Cholesterol, fasting blood glucose, physical activity, BMI, low-density lipoprotein cholesterol, total cholesterol, dietary habits, and family history of stroke. Using Youden’s index as the optimal cutoff, the RF model stratified individuals into high-risk (>0.789) and low-risk (≤0.789) groups with robust discrimination.</div></div><div><h3>Conclusions</h3><div>The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model, providing an effective tool for risk stratification in primary stroke prevention in community settings.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 558-565"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ijnss.2025.10.012
Aaron Aytona Funa, Renz Alvin Emberga Gabay
{"title":"Comment on Numsang et al. (2025) ‘Effects of a culture-specific behavior modification program on glycated hemoglobin and blood pressure among adults with diabetes and hypertension: A randomized controlled trial’","authors":"Aaron Aytona Funa, Renz Alvin Emberga Gabay","doi":"10.1016/j.ijnss.2025.10.012","DOIUrl":"10.1016/j.ijnss.2025.10.012","url":null,"abstract":"","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 601-602"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ijnss.2025.10.010
Yu Liu , Jingjing Chen , Xianhui Lin , Jihong Song , Shaohua Chen
Objectives
Chemotherapy-induced nausea and vomiting (CINV) is a common adverse effect among breast cancer patients, significantly affecting quality of life. Existing evidence on the prevention, assessment, and management of this condition is fragmented and inconsistent. This study constructed a CINV knowledge graph using a large language model (LLM) to integrate nursing and medical evidence, thereby supporting systematic clinical decision-making.
Methods
A top-down approach was adopted. 1) Knowledge base preparation: Nine databases and eight guideline repositories were searched up to October 2024 to include guidelines, evidence summaries, expert consensuses, and systematic reviews screened by two researchers. 2) Schema design: Referring to the Unified Medical Language System, Systematized Nomenclature of Medicine - Clinical Terms, and the Nursing Intervention Classification, entity and relation types were defined to build the ontology schema. 3) LLM-based extraction and integration: Using the Qwen model under the CRISPE framework, named entity recognition, relation extraction, disambiguation, and fusion were conducted to generate triples and visualize them in Neo4j. Four expert rounds ensured semantic and logical consistency. Model performance was evaluated using precision, recall, F1-score, and 95 % confidence interval (95 %CI) in Python 3.11.
Result
A total of 47 studies were included (18 guidelines, two expert consensuses, two evidence summaries, and 25 systematic reviews). The Qwen model extracted 273 entities and 289 relations; after expert validation, 238 entities and 242 relations were retained, forming 244 triples. The ontology comprised nine entity types and eight relation types. The F1-scores for named entity recognition and relation extraction were 82.97 (95 %CI: 0.820, 0.839) and 85.54 (95 %CI: 0.844, 0.867), respectively. The average node degree was 2.03, with no isolated nodes.
Conclusion
The LLM-based CINV knowledge graph achieved structured integration of nursing and medical evidence, offering a novel, data-driven tool to support clinical nursing decision-making and advance intelligent healthcare.
{"title":"Development of a large language model–based knowledge graph for chemotherapy-induced nausea and vomiting in breast cancer and its implications for nursing","authors":"Yu Liu , Jingjing Chen , Xianhui Lin , Jihong Song , Shaohua Chen","doi":"10.1016/j.ijnss.2025.10.010","DOIUrl":"10.1016/j.ijnss.2025.10.010","url":null,"abstract":"<div><h3>Objectives</h3><div>Chemotherapy-induced nausea and vomiting (CINV) is a common adverse effect among breast cancer patients, significantly affecting quality of life. Existing evidence on the prevention, assessment, and management of this condition is fragmented and inconsistent. This study constructed a CINV knowledge graph using a large language model (LLM) to integrate nursing and medical evidence, thereby supporting systematic clinical decision-making.</div></div><div><h3>Methods</h3><div>A top-down approach was adopted. 1) Knowledge base preparation: Nine databases and eight guideline repositories were searched up to October 2024 to include guidelines, evidence summaries, expert consensuses, and systematic reviews screened by two researchers. 2) Schema design: Referring to the Unified Medical Language System, Systematized Nomenclature of Medicine - Clinical Terms, and the Nursing Intervention Classification, entity and relation types were defined to build the ontology schema. 3) LLM-based extraction and integration: Using the Qwen model under the CRISPE framework, named entity recognition, relation extraction, disambiguation, and fusion were conducted to generate triples and visualize them in Neo4j. Four expert rounds ensured semantic and logical consistency. Model performance was evaluated using precision, recall, F1-score, and 95 % confidence interval (95 %<em>CI</em>) in Python 3.11.</div></div><div><h3>Result</h3><div>A total of 47 studies were included (18 guidelines, two expert consensuses, two evidence summaries, and 25 systematic reviews). The Qwen model extracted 273 entities and 289 relations; after expert validation, 238 entities and 242 relations were retained, forming 244 triples. The ontology comprised nine entity types and eight relation types. The F1-scores for named entity recognition and relation extraction were 82.97 (95 %<em>CI</em>: 0.820, 0.839) and 85.54 (95 %<em>CI</em>: 0.844, 0.867), respectively. The average node degree was 2.03, with no isolated nodes.</div></div><div><h3>Conclusion</h3><div>The LLM-based CINV knowledge graph achieved structured integration of nursing and medical evidence, offering a novel, data-driven tool to support clinical nursing decision-making and advance intelligent healthcare.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 524-531"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This review aimed to systematically analyze the technological frameworks, application scenarios, and outcomes of large language model-driven agents (LLMDAs) in nursing practice, and to summarize ethical, technological, and practical challenges, guiding future research and clinical implementation.
Methods
This scoping review was conducted following the JBI guidelines. Five databases (PubMed, Embase, Web of Science, APA PsycNet, Cochrane Library) were systematically searched for peer-reviewed English-language studies from inception until September 9, 2025. Eligible studies were screened by title and abstract, with full-text assessments conducted independently by two reviewers.
Results
Twenty-five studies published between 2023 and 2025 were included, involving nine countries, primarily China (n = 9) and the United States (n = 9). Technological architectures were categorized into three types: collaborative models for solving complex tasks through multi-agent division of labor; augmentative models to enhance the accuracy of information outputs; and interactive models focusing on natural interactions and robotic task execution. Application scenarios included clinical, home-based, and community care. Studies indicated that LLMDAs can enhance diagnostic accuracy, optimize resource allocation, and improve patient experience. Primary ethical challenges identified included data privacy, reliability of generated content, and ambiguous attribution of responsibility.
Conclusions
LLMDAs offer a novel paradigm for intelligent transformation in nursing care through integrative technological frameworks. They demonstrate considerable potential in enhancing clinical decision-making accuracy, efficiency of care delivery, and patient satisfaction. Addressing existing ethical, technical, and practical challenges is essential for facilitating broader clinical adoption.
目的系统分析大型语言模型驱动智能体（llmda）在护理实践中的技术框架、应用场景和结果，总结其在伦理、技术和实践方面面临的挑战，指导未来的研究和临床应用。方法本综述按照JBI指南进行。系统检索了5个数据库（PubMed, Embase, Web of Science, APA PsycNet, Cochrane Library），从研究开始到2025年9月9日进行同行评议的英语研究。通过标题和摘要筛选符合条件的研究，由两名审稿人独立进行全文评估。结果纳入2023 - 2025年间发表的25篇研究，涉及9个国家，主要是中国（n = 9）和美国（n = 9）。技术架构可分为三类：通过多智能体分工解决复杂任务的协作模型；增强模型，提高信息输出的准确性；以及专注于自然交互和机器人任务执行的交互模型。应用场景包括临床、家庭和社区护理。研究表明，LLMDAs可以提高诊断准确性，优化资源配置，改善患者体验。确定的主要伦理挑战包括数据隐私、生成内容的可靠性和模糊的责任归属。结论sllmda通过整合的技术框架为护理智能化转型提供了一种新的范式。它们在提高临床决策准确性、护理服务效率和患者满意度方面显示出相当大的潜力。解决现有的伦理、技术和实践挑战对于促进更广泛的临床应用至关重要。
{"title":"Large language model-driven agents in nursing practice: A scoping review","authors":"Xinglin Zheng , Huina Zou , Linjing Wu , Peihuang Dong , Wenhui Yuan , Yuan Chen","doi":"10.1016/j.ijnss.2025.10.007","DOIUrl":"10.1016/j.ijnss.2025.10.007","url":null,"abstract":"<div><h3>Objectives</h3><div>This review aimed to systematically analyze the technological frameworks, application scenarios, and outcomes of large language model-driven agents (LLMDAs) in nursing practice, and to summarize ethical, technological, and practical challenges, guiding future research and clinical implementation.</div></div><div><h3>Methods</h3><div>This scoping review was conducted following the JBI guidelines. Five databases (PubMed, Embase, Web of Science, APA PsycNet, Cochrane Library) were systematically searched for peer-reviewed English-language studies from inception until September 9, 2025. Eligible studies were screened by title and abstract, with full-text assessments conducted independently by two reviewers.</div></div><div><h3>Results</h3><div>Twenty-five studies published between 2023 and 2025 were included, involving nine countries, primarily China (<em>n</em> = 9) and the United States (<em>n</em> = 9). Technological architectures were categorized into three types: collaborative models for solving complex tasks through multi-agent division of labor; augmentative models to enhance the accuracy of information outputs; and interactive models focusing on natural interactions and robotic task execution. Application scenarios included clinical, home-based, and community care. Studies indicated that LLMDAs can enhance diagnostic accuracy, optimize resource allocation, and improve patient experience. Primary ethical challenges identified included data privacy, reliability of generated content, and ambiguous attribution of responsibility.</div></div><div><h3>Conclusions</h3><div>LLMDAs offer a novel paradigm for intelligent transformation in nursing care through integrative technological frameworks. They demonstrate considerable potential in enhancing clinical decision-making accuracy, efficiency of care delivery, and patient satisfaction. Addressing existing ethical, technical, and practical challenges is essential for facilitating broader clinical adoption.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 532-540"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.ijnss.2025.10.002
Bridget Webb , Suzy Walter
Objectives
This study aimed to establish the concept of overcoming exhaustion, providing a reference basis for nursing management and conducting related nursing research.
Methods
Liehr and Smith’s three-phase, nine-step concept-building process was used to create the concept of overcoming exhaustion. The nine steps were as follows: 1) write a practice story; 2) name the emerging concept; 3) select a theoretical lens; 4) link concept to literature; 5) gather a concept story; 6) identify final core qualities; 7) formulate concept definition; 8) create a concept model; and 9) specify the concept building synthesis.
Results
The concept of overcoming exhaustion was identified based on the elements of a practice story, the life experiences of nurses who struggle with the demands of caring for their patients, their families, and themselves. The theory of self-transcendence was then recognized as the theoretical lens from which to ground the concept. The core qualities, despair and moments of calmness, were derived from the literature and confirmed through a concept story. A definition of the concept integrating the core qualities was formed: overcoming exhaustion involves realizing moments of calmness amidst despair. A model was created to demonstrate the relationship between core qualities, despair, and moments of calmness.
Conclusions
The concept of overcoming exhaustion was developed and, through the concept-building process, was defined as realizing moments of calmness amidst despair. By identifying the complexities of overcoming exhaustion, this work lays the foundation for a future research program to develop understanding and interventions that support nurse well-being in the context of ongoing personal and professional demands.
{"title":"Overcoming exhaustion: Building a conceptual foundation for nursing research","authors":"Bridget Webb , Suzy Walter","doi":"10.1016/j.ijnss.2025.10.002","DOIUrl":"10.1016/j.ijnss.2025.10.002","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to establish the concept of overcoming exhaustion, providing a reference basis for nursing management and conducting related nursing research.</div></div><div><h3>Methods</h3><div>Liehr and Smith’s three-phase, nine-step concept-building process was used to create the concept of overcoming exhaustion. The nine steps were as follows: 1) write a practice story; 2) name the emerging concept; 3) select a theoretical lens; 4) link concept to literature; 5) gather a concept story; 6) identify final core qualities; 7) formulate concept definition; 8) create a concept model; and 9) specify the concept building synthesis.</div></div><div><h3>Results</h3><div>The concept of overcoming exhaustion was identified based on the elements of a practice story, the life experiences of nurses who struggle with the demands of caring for their patients, their families, and themselves. The theory of self-transcendence was then recognized as the theoretical lens from which to ground the concept. The core qualities, despair and moments of calmness, were derived from the literature and confirmed through a concept story. A definition of the concept integrating the core qualities was formed: overcoming exhaustion involves realizing moments of calmness amidst despair. A model was created to demonstrate the relationship between core qualities, despair, and moments of calmness.</div></div><div><h3>Conclusions</h3><div>The concept of overcoming exhaustion was developed and, through the concept-building process, was defined as realizing moments of calmness amidst despair. By identifying the complexities of overcoming exhaustion, this work lays the foundation for a future research program to develop understanding and interventions that support nurse well-being in the context of ongoing personal and professional demands.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 588-592"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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