International Journal of Nursing Sciences最新文献

{"title":"Increasing breastfeeding literacy: A preliminary study to develop an AI-based chatbot","authors":"Kamila Rosamilia Kantovitz ,&nbsp;Maria Eduarda Mattoso ,&nbsp;Maria Davoli Meyer ,&nbsp;Marcella Armbruster de Araújo ,&nbsp;Priscila Alves Giovani ,&nbsp;Valentin Martinez ,&nbsp;Nileshkumar Dubey ,&nbsp;Francisco Humberto Nociti Jr ,&nbsp;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}

{"title":"Toward a nurse-oriented management framework for premature ovarian insufficiency: Integration of guidelines and consensus recommendations","authors":"Li Jiang ,&nbsp;Qianjun Xia ,&nbsp;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}

{"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,&nbsp;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}

{"title":"Development and validation of a stroke risk prediction model using regional healthcare big data and machine learning","authors":"Yunxia Duan ,&nbsp;Rui Wang ,&nbsp;Yumei Sun ,&nbsp;Wendi Zhu ,&nbsp;Yi Li ,&nbsp;Na Yu ,&nbsp;Yu Zhu ,&nbsp;Peng Shen ,&nbsp;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 (&gt;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 (&gt;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}

{"title":"Cognitive and physical functions among Chinese community-dwelling older adults with motoric cognitive risk syndrome: A prospective cohort study","authors":"Junhong Wu,&nbsp;Xinyu Yao,&nbsp;Xing Wu,&nbsp;Yamei Bai,&nbsp;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> &lt; 0.001) and 2015 (Group × time: <em>B</em> = 0.37, <em>P</em> &lt; 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}

{"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 ,&nbsp;Jingjing Chen ,&nbsp;Xianhui Lin ,&nbsp;Jihong Song ,&nbsp;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}

{"title":"Large language model-driven agents in nursing practice: A scoping review","authors":"Xinglin Zheng ,&nbsp;Huina Zou ,&nbsp;Linjing Wu ,&nbsp;Peihuang Dong ,&nbsp;Wenhui Yuan ,&nbsp;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}

{"title":"Nursing Retrieval-Augmented Generation: Retrieval augmented generation for nursing question answering with large language models","authors":"Liping Xiong ,&nbsp;Qiqiao Zeng ,&nbsp;Weixiang Luo ,&nbsp;Ronghui Liu","doi":"10.1016/j.ijnss.2025.10.005","DOIUrl":"10.1016/j.ijnss.2025.10.005","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to develop a Nursing Retrieval-Augmented Generation (NurRAG) system based on large language models (LLMs) and to evaluate its accuracy and clinical applicability in nursing question answering.</div></div><div><h3>Methods</h3><div>A multidisciplinary team consisting of nursing experts, artificial intelligence researchers, and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation. The system included four functional modules: 1) construction of a nursing knowledge base through document normalization, embedding, and vector indexing; 2) nursing question filtering using a supervised classifier; 3) semantic retrieval and re-ranking for evidence selection; and 4) evidence-conditioned language model generation to produce citation-based nursing answers. The system was securely deployed on hospital intranet servers using Docker containers. Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs. Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation – Longest Common Subsequence (ROUGE-L), and clinical correctness was measured using Accuracy.</div></div><div><h3>Results</h3><div>The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models. For ChatGLM2-6B, ROUGE-L increased from (30.73 ± 1.48) % to (64.27 ± 0.27) %, and accuracy increased from (49.08 ± 0.92) % to (75.83 ± 0.35) %. For LLaMA2-7B, ROUGE-L increased from (28.76 ± 0.89) % to (60.33 ± 0.21) %, and accuracy increased from (43.27 ± 0.83) % to (73.29 ± 0.33) %. All differences were statistically significant (<em>P</em> &lt; 0.001). A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based, guideline-concordant nursing responses.</div></div><div><h3>Conclusion</h3><div>The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate, reliable, and traceable evidence-based nursing answers. The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access, support evidence-based nursing decision-making, and promote the safe application of artificial intelligence in nursing practice.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 516-523"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600575","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}

{"title":"Overcoming exhaustion: Building a conceptual foundation for nursing research","authors":"Bridget Webb ,&nbsp;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}

{"title":"Trajectories of cumulative fluid balance and the association with pressure injuries in ICU patients","authors":"Xiangping Chen,&nbsp;Peiqi Liu,&nbsp;Bingyan Zhu,&nbsp;Xiumin Qiu,&nbsp;Wei Yu,&nbsp;Yuewen Lao,&nbsp;Xiaoyan Gong,&nbsp;Yiyu Zhuang","doi":"10.1016/j.ijnss.2025.10.004","DOIUrl":"10.1016/j.ijnss.2025.10.004","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to investigate the longitudinal trajectories of cumulative fluid balance (CFB) in intensive care unit (ICU) patients and analyze the relationship between different trajectory groups and the occurrence of pressure injuries (PIs).</div></div><div><h3>Methods</h3><div>In this retrospective longitudinal study, we obtained health-related data from the Medical Information Mart for Intensive Care IV database, including sociodemographic, disease-related variables, and ICU treatment variables. The daily CFB adjusted for body weight was calculated, and the occurrence of PIs during the ICU stay was recorded. A group-based trajectory model was used to explore the different CFB trajectories. Binary logistic regression was used to analyze the relationship between the CFB trajectory group and PIs.</div></div><div><h3>Results</h3><div>Among the 4,294 included participants, we identified four distinct trajectories of CFB in ICU patients: the rapid accumulation group (12.5 %), the slow accumulation group (28.5 %), the neutral balance group (41.7 %), and the negative decrease group (17.3 %). After adjusting for some sociodemographic, disease-related variables, and ICU treatment variables, the rapid accumulation group had an <em>OR</em> of 1.63 (95 %<em>CI</em>: 1.30, 2.04) for all stages of PIs and an <em>OR</em> of 1.36 (95 %<em>CI</em>: 1.08, 1.72) for stage II or higher PIs compared to the neutral balance group.</div></div><div><h3>Conclusions</h3><div>Four unique trajectories of CFB were identified among patients in the ICU, including rapid accumulation, slow accumulation, neutral balance, and negative decrease. Rapid accumulation independently increased the risk of PIs during ICU stay.</div></div>","PeriodicalId":37848,"journal":{"name":"International Journal of Nursing Sciences","volume":"12 6","pages":"Pages 566-572"},"PeriodicalIF":3.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600618","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}