Journal of Nursing Scholarship最新文献

{"title":"Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure","authors":"Sena Chae, Anahita Davoudi, Jiyoun Song, Lauren Evans, Kathryn H. Bowles, Margaret V. Mcdonald, Yolanda Barrón, Se Hee Min PhD, RN, Sungho Oh PhD, Danielle Scharp MSN, RN, Zidu Xu MMed, BS, RN, Maxim Topaz","doi":"10.1111/jnu.13030","DOIUrl":"10.1111/jnu.13030","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real-time artificial intelligence (AI)-based CDS tool.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note–derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing–driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study discusses the creation of a time-series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"165-177"},"PeriodicalIF":2.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reflections on nursing leadership in socio-contextual and interconnected global scenarios","authors":"Alessandro Stievano RN, PhD, FAAN, Franklin Shaffer RN, EdD, FAAN","doi":"10.1111/jnu.13031","DOIUrl":"10.1111/jnu.13031","url":null,"abstract":"","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"56 6","pages":"755-756"},"PeriodicalIF":2.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142585026","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":"Precision health: Determining the capacity of practicing nurses across the United States.","authors":"Evangeline Fangonil-Gagalang, Mary Anne Schultz, Laurie A Huryk, Pamela A Payne, Anna E Schoenbaum, Kimberly Velez","doi":"10.1111/jnu.13028","DOIUrl":"https://doi.org/10.1111/jnu.13028","url":null,"abstract":"<p><strong>Introduction: </strong>Precision Health (PH) holds the promise of revolutionizing healthcare by enabling personalized disease prevention and management through the integration of genomic data, lifestyle factors, environmental influences, and other social determinants of health (SDoH). However, the absence of a baseline assessment of knowledge, skills, and attitudes (KSAs) of practicing nurses' capacity for PH hinders its integration. The purpose of this study is to determine the capacity of practicing Registered Nurses (RNs) for PH across the United States and to assess the validity and reliability of a tool designed for this use-the Precision Health Nurse Capacity Scale (PHNCS).</p><p><strong>Design/method: </strong>A descriptive exploratory study was conducted to evaluate the capacity of practicing RNs for this evolving phenomenon, PH, using a convenience sample. The survey was sent via email and made available to all members of the American Nurses Association (ANA) who work in a variety of practice environments. The ANA represents the over 4 million nurses practicing in the United States.</p><p><strong>Results: </strong>The majority of nurse respondents felt it is important for nurses to become more educated about all aspects of PH including SDoH but they lack confidence in the integration of PH. The PHNCS was found to be a valid and reliable tool in measuring the capacity of nurses to practice PH.</p><p><strong>Conclusion: </strong>The incorporation of PH into nursing practice suffers an immediate impediment: the lack of know-how of the US nursing workforce. This inaugural data on KSAs for PH establishes a logical baseline from which the requisite education and training should commence.</p><p><strong>Clinical relevance: </strong>Precision Health is an emerging healthcare approach in the United States and globally. Enabling it will require a nursing workforce prepared with the requisite KSAs. Determining the capacity of the nursing workforce is a foundational step to begin this process.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512539","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":"Effectiveness of integrated care models for stroke patients: A systematic review and meta-analysis.","authors":"Beixue Liu, Jingyi Cai, Lanshu Zhou","doi":"10.1111/jnu.13027","DOIUrl":"https://doi.org/10.1111/jnu.13027","url":null,"abstract":"<p><strong>Introduction: </strong>Given that stroke is a leading cause of disability and mortality worldwide, there is an urgent need for a coordinated healthcare approach to mitigate its effects. The objectives of this study were to perform a systematic review and meta-analysis of stroke integrated care models and develop recommendations for a representative model.</p><p><strong>Design: </strong>A systematic review and meta-analysis.</p><p><strong>Methods: </strong>The literature search identified randomized controlled trials comparing integrated care models with standard care for stroke patients. The included studies followed PICOs inclusion criteria. The qualitative analysis included creating a flowchart for the literature screening process, and tables detailing the basic characteristics of the included studies, the adherence to the ten principles and the results of the quality assessments. Subsequently, quantitative meta-analytical procedures were conducted to statistically pool the data and quantify the effects of the integrated care models on stroke patients' health-related quality of life, activities of daily living, and depression. The China National Knowledge Infrastructure (CNKI), Wanfang Data, Chongqing VIP Chinese Science and Technology Periodical Database (VIP), China Biology Medicine Disc (CBMDISC), Cochrane Library, Cumulated Index to Nursing and Allied Health Literature (CINAHL), PubMed, Web of Science, Embase, Google Scholar, and Clinical Trials were searched from inception to March 13, 2024.</p><p><strong>Results: </strong>Of the 2547 obtained articles, 19 were systematically reviewed and 15 were included in the meta-analysis. The integrated care models enhanced stroke patients' health-related quality of life, ability to perform activities of daily living, and reduced depression. Adherence to the 10 principles varied: comprehensive services, patient focus, and standardized care delivery had strong implementation, while gaps were noted in geographic coverage, information systems, governance structures, and financial management.</p><p><strong>Conclusion: </strong>Integrated care models improve outcomes for stroke patients and adherence to the 10 principles is vital for their implementation success. This study's findings call for a more standardized approach to implementing integrated care models, emphasizing the need for integrated services, patient-centred care, and interdisciplinary collaboration, while also addressing the identified gaps in terms of integration efforts.</p><p><strong>Clinical relevance: </strong>This study provides evidence-based recommendations on the most effective integrated care approaches for stroke patients, potentially leading to better patient outcomes, reduced healthcare costs, and improved quality of life.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309055","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":"Decoding machine learning in nursing research: A scoping review of effective algorithms","authors":"Jeeyae Choi PhD, RN,&nbsp;Hanjoo Lee MS,&nbsp;Yeounsoo Kim-Godwin PhD, RN","doi":"10.1111/jnu.13026","DOIUrl":"10.1111/jnu.13026","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Introduction&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI-integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The scoping review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A structured search was performed across seven databases according to PRISMA-ScR: PubMed, EMBASE, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Twenty-six articles published between 2019 and 2023 met the inclusion and exclusion criteria, and 46% of studies were conducted in the US. The average MERSQI score was 12.2, indicative of moderate- to high-quality studies. The most used ML algorithm was Random Forest. The four second-most used were logistic regression, least absolute shrinkage and selection operator, decision tree, and support vector machine. Most ML models were evaluated by calculating sensitivity (recall)/specificity, accuracy, receiver operating characteristic (ROC), area under the ROC (AUROC), and positive/negative prediction value (precision). Half of the studies focused on nursing staff or students and hospital readmission or emergency department visits. Only 11 articles reported the most effective ML algorithm(s).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The scoping review provides insights into the current status of ML research in nursing and recognition of its significance in nursing research, confirming the benefits of ML in healthcare. Recommendations include incorporating experimental designs in research studies to optimize the use of ML models across various nursing domains.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Clinical Relevance&lt;/h3&gt;\u0000 \u0000 ","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"119-129"},"PeriodicalIF":2.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jnu.13026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies","authors":"Nayeon Yi PhD Candidate, RN,&nbsp;Dain Baik PhD Candidate, RN,&nbsp;Gumhee Baek PhD Candidate, RN","doi":"10.1111/jnu.13024","DOIUrl":"10.1111/jnu.13024","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>Systematic review of prospective studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Protocol Registration</h3>\u0000 \u0000 <p>We have registered our review in PROSPERO (registration number: CRD 42023415232).</p>\u0000 </section>\u0000 </div>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"105-118"},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jnu.13024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning methods to discover hidden patterns in well-being and resilience for healthy aging","authors":"Robin R. Austin PhD, DNP, DC, RN, NI-BC, FAMIA, FNAP,&nbsp;Ratchada Jantraporn PhD, MS, RN,&nbsp;Martin Michalowski PhD, FAMIA,&nbsp;Jenna Marquard PhD, FACMI","doi":"10.1111/jnu.13025","DOIUrl":"10.1111/jnu.13025","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>A whole person approach to healthy aging can provide insight into social factors that may be critical. Digital technologies, such as mobile health (mHealth) applications, hold promise to provide novel insights for healthy aging and the ability to collect data between clinical care visits. Machine learning/artificial intelligence methods have the potential to uncover insights into healthy aging. Nurses and nurse informaticians have a unique lens to shape the future use of this technology.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The purpose of this research was to apply machine learning methods to MyStrengths+MyHealth de-identified data (<i>N</i> = 988) for adults 45 years of age and older. An exploratory data analysis process guided this work.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Overall (<i>n</i> = 988), the average Strength was 66.1% (SD = 5.1), average Challenges 66.5% (SD = 7.5), and average Needs 60.06% (SD = 3.1). There was a significant difference between Strengths and Needs (<i>p</i> &lt; 0.001), between Challenges and Needs (<i>p</i> &lt; 0.001), and no significant differences between average Strengths and Challenges. Four concept groups were identified from the data (Thinking, Moving, Emotions, and Sleeping). The <i>Thinking</i> group had the most statistically significant challenges (11) associated with having at least one <i>Thinking</i> Challenge and the highest average Strengths (66.5%) and Needs (83.6%) compared to the other groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This retrospective analysis applied machine learning methods to de-identified whole person health resilience data from the MSMH application. Adults 45 and older had many Strengths despite numerous Challenges and Needs. The Thinking group had the highest Strengths, Challenges, and Needs, which aligns with the literature and highlights the co-occurring health challenges experienced by this group. Machine learning methods applied to consumer health data identify unique insights applicable to specific conditions (e.g., cognitive) and healthy aging. The next steps involve testing personalized interventions with nurses leading artificial intelligence integration into clinical care.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"72-81"},"PeriodicalIF":2.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are we making the most of safe staffing research.","authors":"Alison Steven, Rafael A Bernardes, Monica Bianchi, Nicola Cornally, Ana Inês Costa, Katja Pursio, Marco Di Nitto, Milko Zanini, Marie-Louise Luiking","doi":"10.1111/jnu.13021","DOIUrl":"https://doi.org/10.1111/jnu.13021","url":null,"abstract":"<p><strong>Introduction: </strong>The uptake of research evidence on staffing issues in nursing by nursing leadership, management and into organizational policies seems to vary across Europe. This study wants to assess this uptake of research evidence.</p><p><strong>Design: </strong>Scoping survey.</p><p><strong>Method: </strong>The presidents of twelve country specific Sigma Chapters within the European Region answered written survey questions about work organisation, national staffing levels, national skill mix levels, staff characteristics, and education.</p><p><strong>Results: </strong>Seven of the 12 chapters could not return complete data, reported that data was unavailable, there was no national policy or only guidance related to some settings.</p><p><strong>Conclusion: </strong>Enhancing the awareness of nursing research and of nursing leaders and managers regarding staffing level evidence is not enough. It seems necessary to encourage nurse leaders to lobby for staffing policies.</p><p><strong>Clinical relevance: </strong>Research evidence on staffing issues in nursing and how it benefits health care is available. In Europe this evidence should be used more to lobby for change in staffing policies.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114473","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":"The impact of gender on the nursing figure and nurses' interprofessional relationships: A multimethod study.","authors":"Loredana Piervisani, Maddalena De Maria, Sabrina Spagnuolo, Patrizia Nazzaro, Gennaro Rocco, Ercole Vellone, Rosaria Alvaro","doi":"10.1111/jnu.13020","DOIUrl":"https://doi.org/10.1111/jnu.13020","url":null,"abstract":"<p><strong>Aims: </strong>To identify the current presence of stereotypes about the nursing profession in Italy and to understand how gendered processes and modalities are regulated and expressed in the physician-nurse dyad, and the implications for professional identity and autonomy.</p><p><strong>Design: </strong>Qualitative multimethod design.</p><p><strong>Methods: </strong>Forty-five interviews were conducted with nurses and physicians. The collected qualitative data underwent automatic textual data analysis using a multidimensional exploratory approach and a gender framework analysis.</p><p><strong>Results: </strong>In Italy, nurses' roles are still associated with gender stereotypes stemming from the predominant male culture, which affects sexual and gender identity, the division of labor, and access to career paths. This leads to disadvantages in the nursing profession, which is heavily dominated by women.</p><p><strong>Conclusion: </strong>Biological differences between sexes generate an unconscious yet shared symbolic gender order composed of negative stereotypes that influence nurses' professional roles and activities. They follow behaviors that enter the work routine and institutionalize organizational processes. These effects are also seen in the asymmetric, limited, and reciprocal interprofessional relationships between male physicians and female nurses, where the former hinders the latter's professional autonomy and access to top positions.</p><p><strong>Implications for the profession: </strong>This survey raises awareness of gender issues and stimulates reflection. It also enables health and nursing organizations to take action to raise gender awareness and education by countering the image of a non-autonomous profession. The analysis of gender processes allows us to identify interventions that can counteract forms of oppression in the work environment that lead to the emergence of nursing as a non-autonomous profession.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094053","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":"Nurses during war: Profiles-based risk and protective factors.","authors":"Hamama Liat, Amit Inbal, Itzhaki Michal","doi":"10.1111/jnu.13019","DOIUrl":"https://doi.org/10.1111/jnu.13019","url":null,"abstract":"<p><strong>Introduction: </strong>Nurses in southern Israel's public hospitals were exposed to unusual traumatic events following the October 7, 2023, Hamas attack on Israel, and the ensuing Swords of Iron War. This study aimed to clarify the complexity of wartime nursing by identifying profiles based on risk factors (i.e., psychological distress and adjustment disorders) and protective factors (i.e., positive affect (PA), resilience, and perceived social support [PSS]).</p><p><strong>Design: </strong>This study utilizes a cross-sectional design.</p><p><strong>Method: </strong>Two hundred nurses at a major public hospital in southern Israel completed self-report questionnaires. A latent profile analysis (LPA) was conducted to identify distinct profiles based on nurses' risk and protective factors. Differences in profiles were examined alongside sociodemographic and occupational variables and traumatic event exposure. The LPA was conducted using MPlus 8.8 Structural Equation Modeling (SEM) software.</p><p><strong>Findings: </strong>Two distinct profiles were identified: \"reactive\" and \"resilient.\" The \"reactive\" group included nurses who had higher risk factor scores (psychological distress and adjustment disorder), whereas the \"resilient\" group included nurses who had higher protective factor scores (PA, resilience, and PSS). Furthermore, nurses in the \"reactive\" group were younger, with greater seniority, worse self-rated health, and a higher frequency of kidnapped family members compared to nurses from the \"resilient\" group.</p><p><strong>Conclusion: </strong>Nurses in wartime are at risk if identified as \"reactive.\" Identifying these profiles can assist in developing effective support practices to help nurses cope with wartime challenges and maintain their mental well-being.</p><p><strong>Clinical relevance: </strong>Healthcare organizations should tailor interventions to prepare and support nurses of various ages and experience levels, during and after conflicts. This approach aims to reduce risk factors and promote protective factors among nurses during wartime.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074444","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}