Journal of Nursing Scholarship最新文献

{"title":"Effectiveness of digital interventions to reduce school-age adolescent sexual risks: A systematic review","authors":"Ana Aguilar-Quesada RN, MSN, Alba Sierra-Yagüe RN, MSN, María González-Cano-Caballero RN, MSN, PhD, José Antonio Zafra-Egea RN, MSN, PhD, Marta Lima-Serrano RN, MSN, PhD","doi":"10.1111/jnu.13015","DOIUrl":"10.1111/jnu.13015","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The increase in risky sexual behaviors among adolescent students has sparked alarm and has become an area of research interest. As adolescents prioritize confidentiality and accessibility, digital interventions are becoming increasingly relevant in sex education. We therefore posed the following research question: Are digital application interventions effective to prevent risky sexual behaviors in school adolescents?</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>A systematic peer review was conducted between January and December 2023 in five databases (PubMed, Web of Science, Scopus, EMBASE, and PsycINFO) without restricting for language or year of publication.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We included randomized control trials or quasi-experimental studies that measured the effectiveness of interventions targeting young people aged 10–19 years or their parents and developed in a school setting. Interventions aimed at young people with intellectual disabilities, learning difficulties, or any disease requiring a specific intervention were excluded.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The search ultimately yielded 27 studies covering a total of 18 digital interventions that demonstrated positive effects, not maintained over time, on knowledge, attitudes, and behaviors, although the latter to a lesser extent.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>We have found very interesting digital interventions with effects, among others, on knowledge, attitudes, and contraceptive use in adolescents. In general, digital interventions have positive effects on knowledge and attitudes, but it is more difficult to modify behaviors with strictly digital interventions or combined with complementary face-to-face sessions or group class activities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We thus believe that digital interventions are adequate to reduce adolescent sexual risk behaviors, and our systematic review facilitates the implementation of these interventions by sharing existing digital interventions that have had positive effects, as well as the main characteristics a digital intervention should possess to reduce sexually risky behaviors in adolescents.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Clinical relevance</h3>\u0000 ","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 2","pages":"342-353"},"PeriodicalIF":2.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jnu.13015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908302","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":"Association between nurses’ personal, professional and work characteristics, and engagement in hospital-based clinical research","authors":"Jennifer Colwill DNP, APRN, CCNS, PCCN,&nbsp;Heather Condo DiCioccio DNP, RNC-MNN, C-ONQS,&nbsp;James F. Bena MS,&nbsp;Shannon L. Morrison MS,&nbsp;Ashley Hall MSN, RN, CMSRN,&nbsp;Visnja Masina DNP, APRN, AGCNS-BC,&nbsp;Robon Vanek MA, MSN, APRN,&nbsp;Nancy M. Albert PhD, CCNS, NE-BC, FAAN","doi":"10.1111/jnu.13010","DOIUrl":"10.1111/jnu.13010","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this study was to assess the associations between demographic, professional and other personal nurse characteristics, social support factors and comfort in conducting research with nurses' level of active participation in clinical research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>A prospective, cross-sectional, correlational design was used.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Clinical nurses working in a multihospital healthcare system were recruited by email to complete an anonymous survey that used multiple valid and reliable scales to assess demographic and professional work characteristics, curiosity, grit, locus of control, perceived social support (for research activities), comfort in conducting research, and level of being research-active. Univariate and multivariable analyses were completed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of 310 participants, 274 (88.4%) were female and mean (SD) age was 42.9 (13.1) years. After condensing 11 levels of research activity to four categories, 179 (57.7%) were not research-active, and 91 (29.4%), 26 (8.3%) and 14 (4.5%) were engaged at low, moderate, and high levels, respectively. Of 78 factors, 69 (88.5%) were associated with being research-active in univariate analyses. In multivariable analysis that adjusted for age, personal experience as a patient, years as a nurse and hours in direct patient care, professionalism characteristics, higher curiosity, internal locus of control, grit perseverance, support of a nurse scientist and nurse friends, and comfort in conducting research remained associated with higher levels of being research-active (all <i>p</i> &lt; 0.01).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Research-active nurses were more likely to be engaged professionally in hospital-based activities beyond their work roles and displayed higher levels of positive psychological characteristics and mentorship that supported research capacity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Clinical Relevance</h3>\u0000 \u0000 <p>Research-active nurses were more likely to have internal factors and external resources that promoted higher levels of being research-active. A strong professional governance model may enhance clinical nurses research activities.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"56 6","pages":"815-825"},"PeriodicalIF":2.9,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861587","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":"Can digital leadership transform AI anxiety and attitude in nurses?","authors":"Sinan Tarsuslu PhD,&nbsp;Ferhat Onur Agaoglu PhD,&nbsp;Murat Bas PhD","doi":"10.1111/jnu.13008","DOIUrl":"10.1111/jnu.13008","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The lack of artificial intelligence applications in nursing education and the nursing profession in Turkey and the need for strategies for integrating artificial intelligence into the nursing profession continues. At this point, there is a need to transform the negative attitudes and anxiety that may occur in nurses.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objectives&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;It was aimed to reorganize the professional transformation in this parallel by analyzing the effect of digital leadership perception, which is explained as how nurses approach digital technologies and innovations and their awareness of how and with which methods they can use these technologies on artificial intelligence anxiety and attitude in the nursing profession.&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 study was designed as descriptive, correlational, and cross-sectional.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Participants&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The research was conducted by reaching 439 nurses working in hospitals operating in three different regions of Turkey by simple random sampling method.&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;In the first part of the data collection tool used in this study, digital leadership scale, artificial intelligence use anxiety, and artificial intelligence attitude scales were used, including questions determining the demographic information of nurses, their relationship with technology, artificial intelligence usage status and its importance in the profession.&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;It was determined that 29.8% of the nurses had a good relationship with technology, 66.3% knew about using artificial intelligence in health, and 27.3% wanted it to be more involved in their lives. It was determined that nurses' perceptions of digital leadership were at a medium level of 46.9% and a high level of 41.7%, 82.7% had a positive attitude towards artificial intelligence, and 82.7% had low or medium level anxiety when their artificial intelligence anxiety status was examined. There was a significant and negative relationship between digital leadership and AI anxiety (&lt;i&gt;r&lt;/i&gt; = −0.434; &lt;i&gt;p&lt;/i&gt; &lt; 0.01), a significant and positive relationship between digital leadership and AI attitude (&lt;i&gt;r&lt;/i&gt; = 0.468; &lt;i&gt;p&lt;/i&gt; &lt; 0.01), and a significant and negative relationship between AI attitude and AI anxiety (&lt;i&gt;r","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"28-38"},"PeriodicalIF":2.9,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861588","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":"Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare","authors":"Michael P. Cary Jr PhD, RN,&nbsp;Sophia Bessias MPH, MSA,&nbsp;Jonathan McCall MS,&nbsp;Michael J. Pencina PhD,&nbsp;Siobahn D. Grady PhD,&nbsp;Kay Lytle DNP, RN,&nbsp;Nicoleta J. Economou-Zavlanos PhD","doi":"10.1111/jnu.13007","DOIUrl":"10.1111/jnu.13007","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The concept of &lt;i&gt;health equity by design&lt;/i&gt; encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of “big data” in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a “real-world” AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative.&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;Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework.&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;The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion and Relevance&lt;/h3&gt;","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"130-139"},"PeriodicalIF":2.9,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141793985","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":"Optimizing pain management in breast cancer care: Utilizing ‘All of Us’ data and deep learning to identify patients at elevated risk for chronic pain","authors":"Jung In Park PhD, RN, FAMIA,&nbsp;Steven Johnson PhD,&nbsp;Lisiane Pruinelli PhD, MSN, RN, FAMIA","doi":"10.1111/jnu.13009","DOIUrl":"10.1111/jnu.13009","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>This study was a retrospective, observational study.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used demographic, diagnosis, and social survey data from the NIH ‘All of Us’ program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Clinical Relevance</h3>\u0000 \u0000 <p>Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"95-104"},"PeriodicalIF":2.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762408","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":"Comparing different scoping and mapping review methodologies: A practical example using the nursing mobile workstation","authors":"Margot Vanmeenen MSc, RN,&nbsp;Julian Hirt PhD, RN,&nbsp;Simon Malfait PhD, RN,&nbsp;Ralph Möhler PhD, RN","doi":"10.1111/jnu.13005","DOIUrl":"10.1111/jnu.13005","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Aims&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To provide (1) an overview of core characteristics of scoping and mapping review methodologies and (2) to illustrate the differences and similarities of these methodologies using literature on nursing mobile workstations.&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;Systematic review.&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;Systematic searches were conducted to identify (1) scoping and mapping review methodologies used in the field of nursing and (2) literature on nursing mobile workstations. For each systematic search, two reviewers independently screened all titles, abstracts, and full texts. We conducted narrative syntheses for both review questions. Publications on scoping and mapping review methodologies in the field of nursing were searched in MEDLINE (PubMed), Web of Science, Scopus, and CINAHL (September 2022). Publications on nursing mobile workstations were searched in MEDLINE (PubMed), CINAHL, and Web of Science (April 2022).&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;We identified six scoping and mapping review methodologies (aim 1): bibliometric analysis, evidence mapping, focused mapping review and synthesis, and scoping review. The methodologies aim to provide a graphical, tabular, or narrative overview without a formal critical assessment of the literature. We provide an overview of key variables that reflect the different focus of these methodologies. We also included 26 publications on nursing mobile workstations (aim 2). Nineteen different terms were used to describe the workstations. An overall definition of the nursing mobile workstation was not found.&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;Scoping and mapping methodologies are regularly applied in nursing research. Although there is overlap between the different methodologies, we found some unique characteristics. Despite the regular use of nursing mobile workstations, little is known about their impact in care processes and important features. Future studies on nursing mobile workstations could explore the impact of the workstations in the care process and the current functions of the workstations. A universal definition of the workstations is warranted.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Clinical Relevance&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Most publications address aspects of practicability of nursing mobile workstations, but we found no universal defin","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"56 6","pages":"802-814"},"PeriodicalIF":2.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jnu.13005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749595","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":"Patient racism toward nurses in a divided society: The case of Jews and Arabs in Israel","authors":"Riki Halamish-Leshem PhD,&nbsp;Ya'arit Bokek-Cohen PhD,&nbsp;Mahdi Tarabeih RN, PhD,&nbsp;Pazit Azuri RN, PhD","doi":"10.1111/jnu.13006","DOIUrl":"10.1111/jnu.13006","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Aim</h3>\u0000 \u0000 <p>This study examines whether racism exists among Jewish and Arab patients in Israel, as reflected in patient preference for receiving treatment from a nurse with the same ethnic background.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>We examine the relationship between racism and the level of trust in a nurse from a different ethnic group than the patient, as well as the preferred level of social distance, in the context of ongoing conflicts between the Jewish majority and the Arab minority in Israel.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A cross-sectional study was conducted using a unique study questionnaire that asked 534 Jewish and 478 Arab respondents to express their preference for an Arab and a Jewish nurse.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among both the Jews and the Arabs, there is a similar tendency of racism toward nurses of the dissimilar ethnic group. This racism was also prevalent among participants who live in a mixed environment or those who studied or are studying and worked or work in a mixed environment. As the trust in nursing staff members from the other group increases, the level of racism decreases. The greater the social distance the participants felt from the members of the other group, the more racist the attitudes they expressed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Both Jews and Arabs preferred to be treated by nurses of their own ethnic group. In contrast to the contact hypothesis theory, participants who live in a mixed environment did not express fewer racist preferences. We conclude with some useful practical suggestions aimed at decreasing racism in health care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Clinical Relevance</h3>\u0000 \u0000 <p>Findings imply that prospective patients prefer to receive nursing care from nurses of their own ethnic group and trust these nurses more than they trust nurses of different ethnic group.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"56 6","pages":"843-853"},"PeriodicalIF":2.9,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621759","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":"Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient–nurse verbal communications in home healthcare settings?","authors":"Jihye Kim Scroggins PhD, RN,&nbsp;Maxim Topaz PhD, RN,&nbsp;Jiyoun Song PhD, RN,&nbsp;Maryam Zolnoori PhD","doi":"10.1111/jnu.13004","DOIUrl":"10.1111/jnu.13004","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Identifying health problems in audio-recorded patient–nurse communication is important to improve outcomes in home healthcare patients who have complex conditions with increased risks of hospital utilization. Training machine learning classifiers for identifying problems requires resource-intensive human annotation.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To generate synthetic patient–nurse communication and to automatically annotate for common health problems encountered in home healthcare settings using GPT-4. We also examined whether augmenting real-world patient–nurse communication with synthetic data can improve the performance of machine learning to identify health problems.&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;Secondary data analysis of patient–nurse verbal communication data in home healthcare settings.&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;The data were collected from one of the largest home healthcare organizations in the United States. We used 23 audio recordings of patient–nurse communications from 15 patients. The audio recordings were transcribed verbatim and manually annotated for health problems (e.g., circulation, skin, pain) indicated in the Omaha System Classification scheme. Synthetic data of patient–nurse communication were generated using the in-context learning prompting method, enhanced by chain-of-thought prompting to improve the automatic annotation performance. Machine learning classifiers were applied to three training datasets: real-world communication, synthetic communication, and real-world communication augmented by synthetic communication.&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;Average &lt;i&gt;F&lt;/i&gt;1 scores improved from 0.62 to 0.63 after training data were augmented with synthetic communication. The largest increase was observed using the XGBoost classifier where &lt;i&gt;F&lt;/i&gt;1 scores improved from 0.61 to 0.64 (about 5% improvement). When trained solely on either real-world communication or synthetic communication, the classifiers showed comparable &lt;i&gt;F&lt;/i&gt;1 scores of 0.62–0.61, respectively.&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;Integrating synthetic data improves machine learning classifiers' ability to identify health problems in home healthcare, with performance comparable to training on real-world data alone, highlighting the potential of synthetic data in healthcare analytics.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"47-58"},"PeriodicalIF":2.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499568","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 quality of clinician and student quality improvement reports: An analysis of 8 years of submissions","authors":"Maureen (Shawn) Kennedy MA, RN, FAAN,&nbsp;Jane Barnsteiner PhD, RN, FAAN","doi":"10.1111/jnu.13003","DOIUrl":"10.1111/jnu.13003","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Many papers reporting on QI projects are not publishable for a variety of reasons. We compared manuscripts submitted as QI reports between June 2014 and June 2016 (prior to publication of the revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) with papers submitted to the <i>American Journal of Nursing</i> between July 2016 and December 2022). The aim was to evaluate any changes in the quality of manuscripts and identify problems that led to rejection; we also compared the quality of students with non-student submissions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a non-randomized descriptive study to evaluate 349 papers submitted as QI project reports between June 2014 and December 2022 using screening templates based on the SQUIRE 2.0 checklist and findings of the INANE Working Group on Student Papers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Manuscripts designated as QI reports accepted for publication increased from 4% during 2014–2016 (T1) to 14% during 2016–2022 (T2); one student submission was accepted. There was a slight decrease in submissions designated as QI that were not QI: 36% of student submissions during T1 and 31% of student submissions during T2. Among clinician submissions, 44% in T1 designated as QI reports were not QI versus 31% submitted during T2<i>.</i> There was a decrease in student submissions that followed the SQUIRE guidelines (36% during T1 to 24% during T2).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Findings demonstrate that by following the SQUIRE 2.0 guidelines, authors submit more complete manuscripts with fewer missing components. However, there are still misconceptions about what constitutes QI versus research and how to report QI initiatives. After comparing the findings from both periods, it is noteworthy that there is essentially the same level of inaccuracy and lack of acceptable manuscripts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Clinical Relevance</h3>\u0000 \u0000 <p>Sharing findings from QI activities through presentations and publications is a vital way of helping spread the learnings from these projects and improve health care for a wider audience. Clinicians, academicians, and students must understand the elements of the SQUIRE guidelines and ensure that this framework is used for both designing and submitting QI projects for publication.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"56 6","pages":"836-842"},"PeriodicalIF":2.9,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472358","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":"Health outcomes in children with prenatal opioid exposure with and without neonatal abstinence syndrome in the first seven years of life: An observational cohort study","authors":"Joshua Lambert PhD, MS,&nbsp;Sara Arter PhD, RN,&nbsp;Henry Duah PhD, MPH, RN,&nbsp;Teenu Xavier PhD, RN,&nbsp;Jon E. Sprague RPH, PhD","doi":"10.1111/jnu.13000","DOIUrl":"10.1111/jnu.13000","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;Prenatal opioid exposure (POE) is a major public health consequence of the opioid epidemic. Long-term health outcomes associated with POE remain unclear, especially for children with POE without a diagnosis of neonatal abstinence syndrome (NAS). Here, we aimed to describe the health outcomes of children with POE and with POE and NAS compared to unexposed children during the first 7 years of life.&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;In this retrospective observational cohort study, children born between 2015 and 2022 were identified from the Maternal and Infant Data Hub (MIDH), a data repository that continuously integrates maternal, neonatal, and pediatric records from two academic medical centers and one pediatric hospital system in the Midwest, USA.&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;International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10 CM) chapters A00-N99 served as outcomes of interest. Annual incidence and crude incidence rate ratios were calculated to explore descriptive differences between the exposed and unexposed groups.&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;The study included 22,002 children, 20,130 (91.5%) of whom were unexposed and 1872 (8.5%) were exposed. Of the 1872 exposed children, 371 (19.8%) received a diagnosis of NAS (POE + NAS) and 1501 were in the POE-NAS group. Across all 7 years, exposed children had a higher incidence of diagnoses in most ICD-10 CM chapters compared to unexposed children. A consistently higher incidence rate ratio of diagnosis was observed in both POE-NAS and POE + NAS groups (vs. unexposed) related to mental and behavioral disorders, eye diagnoses, and diseases of the musculoskeletal system and gastrointestinal systems.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;POE is associated with an increased risk of diagnoses in a number of ICD-10 CM chapters throughout childhood. These findings underscore the need for early screening and targeted interventions to support exposed children and improve their well-being. Further research is required to explore underlying mechanisms and develop preventive measures for at-risk populations.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Clinical Relevance&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Understanding the conditions more often diagnosed in children with prenatal opioid exposure will help to improve care p","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"56 6","pages":"767-779"},"PeriodicalIF":2.9,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jnu.13000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508695","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}