JMIR nursing最新文献

{"title":"Unobtrusive Nighttime Movement Monitoring to Support Nursing Home Continence Care: Algorithm Development and Validation Study.","authors":"Hannelore Strauven, Chunzhuo Wang, Hans Hallez, Vero Vanden Abeele, Bart Vanrumste","doi":"10.2196/58094","DOIUrl":"https://doi.org/10.2196/58094","url":null,"abstract":"<p><strong>Background: </strong>The rising prevalence of urinary incontinence (UI) among older adults, particularly those living in nursing homes (NHs), underscores the need for innovative continence care solutions. The implementation of an unobtrusive sensor system may support nighttime monitoring of NH residents' movements and, more specifically, the agitation possibly associated with voiding events.</p><p><strong>Objective: </strong>This study aims to explore the application of an unobtrusive sensor system to monitor nighttime movement, integrated into a care bed with accelerometer sensors connected to a pressure-redistributing care mattress.</p><p><strong>Methods: </strong>A total of 6 participants followed a 7-step protocol. The obtained dataset was segmented into 20-second windows with a 50% overlap. Each window was labeled with 1 of the 4 chosen activity classes: in bed, agitation, turn, and out of bed. A total of 1416 features were selected and analyzed with an XGBoost algorithm. At last, the model was validated using leave one subject out cross-validation (LOSOCV).</p><p><strong>Results: </strong>The trained model attained a trustworthy overall F1-score of 79.56% for all classes and, more specifically, an F1-score of 79.67% for the class \"Agitation.\"</p><p><strong>Conclusions: </strong>The results from this study provide promising insights in unobtrusive nighttime movement monitoring. The study underscores the potential to enhance the quality of care for NH residents through a machine learning model based on data from accelerometers connected to a viscoelastic care mattress, thereby driving progress in the field of continence care and artificial intelligence-supported health care for older adults.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"7 ","pages":"e58094"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Educators' perceptions and experiences of online teaching to foster caring professions students' development of virtual caring skills: A sequential explanatory mixed-methods study.","authors":"Lorelli Nowell, Sonja Johnston, Sara Dolan, Michele Jacobsen, Diane Lorenzetti, Elizabeth Oddone Paolucci","doi":"10.2196/64548","DOIUrl":"https://doi.org/10.2196/64548","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Professionals in caring disciplines have been pivotal in advancing virtual care, which leverages remote technologies to deliver effective support and services from a distance. Educators in these caring professions are required to teach students the skills and competencies needed to provide high-quality and effective care and as virtual care becomes more integral, educators must equip students in these fields with both interpersonal and technological skills, bridging traditional hands-on learning with digital literacy. However, there is a gap in evidence exploring educators' perceptions and experiences of teaching caring professions students about virtual caring skills within online environments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To better understand caring professional educators' online teaching experiences to foster student development of virtual caring skills and competencies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We employed a sequential explanatory mixed-methods approach, that integrated a cross-sectional survey and individual interviews with educators from caring professions, to better understand caring professional educators' online teaching experiences to foster student development of virtual caring skills and competencies. The survey's primary objectives were to examine the various elements of existing e-learning opportunities, delve into educators' perspectives and encounters with these opportunities, and identify the factors that either facilitated or hindered online teaching practices to support students in developing virtual caring skills and competencies. The individual interview guides were based on survey findings and a systematic review of the evidence to gain deeper insights into educators' experiences and perspectives.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 82 survey and 8 interview participants were drawn from educators from Education, Medicine, Nursing, and Social Work. Various instructional methods were utilized to help students develop virtual caring skills including reflections on learning, online modules, online discussion boards, demonstrations of remote care and consultation with clients. There was a statistically significant difference between educators' level of experience teaching online and their satisfaction with online teaching and learning technologies (p &lt; .001) and between educators' faculties (department) and their satisfaction with online teaching and learning technologies (p = .001). Participants identified barriers (time constraints, underdeveloped curriculum, decreased student engagement and limited access to virtual caring equipment and technology), facilitators (clearly defined learning objectives, technology software and support, teaching support, stakeholder engagement, and flexibility), and principles of teaching virtual caring skills in online environments (connection, interaction, compassion, empathy, care, and vulnerability).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our stud","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Visitor Expectations of AI Nursing Robots in Hospital Settings: Cross-Sectional Study Using the Kano Model.","authors":"Aimei Kang, XiuLi Wu","doi":"10.2196/59442","DOIUrl":"10.2196/59442","url":null,"abstract":"<p><strong>Background: </strong>Globally, the rates at which the aging population and the prevalence of chronic diseases are increasing are substantial. With declining birth rates and a growing percentage of older individuals, the demand for nursing staff is steadily rising. However, the shortage of nursing personnel has been a long-standing issue. In recent years, numerous researchers have advocated for the implementation of nursing robots as a substitute for traditional human labor.</p><p><strong>Objective: </strong>This study analyzes hospital visitors' attitudes and priorities regarding the functional areas of artificial intelligence (AI) nursing robots based on the Kano model. Building on this analysis, recommendations are provided for the functional optimization of AI nursing robots, aiming to facilitate their adoption in the nursing field.</p><p><strong>Methods: </strong>Using a random sampling method, 457 hospital visitors were surveyed between December 2023 and March 2024 to compare the differences in demand for AI nursing robot functionalities among the visitors.</p><p><strong>Results: </strong>A comparative analysis of the Kano attribute quadrant diagrams showed that visitors seeking hospitalization prioritized functional aspects that enhance medical activities. In contrast, visitors attending outpatient examinations focused more on functional points that assist in medical treatment. Additionally, visitors whose purpose was companionship and care emphasized functional aspects that offer psychological and life support to patients.</p><p><strong>Conclusions: </strong>AI nursing robots serve various functional areas and cater to diverse audience groups. In the future, it is essential to thoroughly consider users' functional needs and implement targeted functional developments to maximize the effectiveness of AI nursing robots.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"7 ","pages":"e59442"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calculating Optimal Patient to Nursing Capacity: Comparative Analysis of Traditional and New Methods.","authors":"Anna Ware, Terri Blumke, Peter Hoover, David Arreola","doi":"10.2196/59619","DOIUrl":"10.2196/59619","url":null,"abstract":"<p><strong>Background: </strong>Optimal nurse staffing levels have been shown to impact patients' prognoses and safety, as well as staff burnout. The predominant method for calculating staffing levels has been patient-to-nurse (P/N) ratios and nursing hours per patient day. However, both methods fall short of addressing the dynamic nature of staffing needs that often fluctuate throughout the day as patients' clinical status changes and new patients are admitted or discharged from the unit.</p><p><strong>Objective: </strong>In this evaluation, the Veterans Affairs Palo Alto Health Care System (VAPAHCS) piloted a new dynamic bed count calculation in an effort to target optimal staffing levels every hour to provide greater temporal resolution on nurse staffing levels within the Veterans Health Administration.</p><p><strong>Methods: </strong>The dynamic bed count uses elements from both the nursing hours per patient day and P/N ratio to calculate current and target staffing levels, every hour, while balancing across nurse types (registered nurses to nurse assistants) to provide improved temporal insight into staff allocation. The dynamic bed count was compared with traditional P/N ratio methods of calculating patient capacity at the VAPAHCS, to assess optimal patient capacity within their acute care ward from January 1, 2023, through May 25, 2023. Descriptive statistics summarized patient capacity variables across the intensive care unit (ICU), medical-surgical ICU, and 3 acute care units. Student t tests (2-tailed) were used to analyze differences between patient capacity measures.</p><p><strong>Results: </strong>Hourly analysis of patient capacity information displayed how the dynamic bed count provided improved temporal resolution on patient capacity. Comparing the dynamic bed count to the P/N ratio, we found the patient capacity, as determined by the P/N ratio, was, on average, higher than that of the dynamic bed count across VAPAHCS acute care units and the medical-surgical ICU (P<.001). For example, in acute care unit 3C, the average dynamic bed count was 21.6 (SD 4.2) compared with a P/N ratio of 28.6 (SD 3.2). This suggests that calculating patient capacity using P/N ratios alone could lead to units taking on more patients than what the dynamic bed count suggests the unit can optimally handle.</p><p><strong>Conclusions: </strong>As a new patient capacity calculation, the dynamic bed count provided additional details and timely information about clinical staffing levels, patient acuity, and patient turnover. Implementing this calculation into the management process has the potential to empower departments to further optimize staffing and patient care.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"7 ","pages":"e59619"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Patient Monitoring at Home in Patients With COVID-19: Narrative Review.","authors":"Justien Cornelis, Wendy Christiaens, Christophe de Meester, Patriek Mistiaen","doi":"10.2196/44580","DOIUrl":"10.2196/44580","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;During the pandemic, health care providers implemented remote patient monitoring (RPM) for patients experiencing COVID-19. RPM is an interaction between health care professionals and patients who are in different locations, in which certain patient functioning parameters are assessed and followed up for a certain duration of time. The implementation of RPM in these patients aimed to reduce the strain on hospitals and primary care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;With this literature review, we aim to describe the characteristics of RPM interventions, report on patients with COVID-19 receiving RPM, and provide an overview of outcome variables such as length of stay (LOS), hospital readmission, and mortality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A combination of different searches in several database types (traditional databases, trial registers, daily [Google] searches, and daily PubMed alerts) was run daily from March 2020 to December 2021. A search update for randomized controlled trials (RCTs) was performed in April 2022.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The initial search yielded more than 4448 articles (not including daily searches). After deduplication and assessment for eligibility, 241 articles were retained describing 164 telemonitoring studies from 160 centers. None of the 164 studies covering 248,431 patients reported on the presence of a randomized control group. Studies described a \"prehosp\" group (96 studies) with patients who had a suspected or confirmed COVID-19 diagnosis and who were not hospitalized but closely monitored at home or a \"posthosp\" group (32 studies) with patients who were monitored at home after hospitalization for COVID-19. Moreover, 34 studies described both groups, and in 2 studies, the description was unclear. In the prehosp and posthosp groups, there were large variations in the number of emergency department (ED) visits (0%-36% and 0%-16%, respectively) and no convincing evidence that RPM leads to less or more ED visits or hospital readmissions (0%-30% and 0%-22%, respectively). Mortality was generally low, and there was weak to no evidence that RPM is associated with lower mortality. Moreover, there was no evidence that RPM shortens previous LOS. A literature update identified 3 small-scale RCTs, which could not demonstrate statistically significant differences in these outcomes. Most papers claimed savings; however, the scientific base for these claims was doubtful. The overall patient experiences with RPM were positive, as patients felt more reassured, although many patients declined RPM for several reasons (eg, technological embarrassment, digital literacy).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Based on these results, there is no convincing evidence that RPM in COVID-19 patients avoids ED visits or hospital readmissions and shortens LOS or reduces mortality. On the other hand, there is no evidence that RPM has adverse outcomes. Further research should focus on developing, impleme","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":" ","pages":"e44580"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Effectiveness of Health Communication Strategies in Nursing: A Mixed Methods Study of Internet, mHealth, and Social Media Versus Traditional Methods.","authors":"Mariwan Qadir Hamarash, Radhwan Ibrahim, Marghoob Hussein Yaas, Mohammed Faris Abdulghani, Osama Al Mushhadany","doi":"10.2196/55744","DOIUrl":"10.2196/55744","url":null,"abstract":"<p><strong>Background: </strong>Effective communication is vital in health care, especially for nursing students who are the future of health care delivery. In Iraq's nursing education landscape, characterized by challenges such as resource constraints and infrastructural limitations, understanding communication modalities is crucial.</p><p><strong>Objective: </strong>This mixed methods study conducted in 2 nursing colleges aims to explore and compare the effectiveness of health communication on the web, through mobile health (mHealth) applications, and via social media among nursing students in Iraq. The research addresses a gap in understanding communication modalities specific to Iraq and explores the perspectives, experiences, and challenges faced by nursing students.</p><p><strong>Methods: </strong>Qualitative interviews were conducted with a purposive sample (n=30), and a structured survey was distributed to a larger sample (n=300) representing diverse educational programs. The study used a nuanced approach to gather insights into the preferences and usage patterns of nursing students regarding communication modalities. The study was conducted between January 12, 2023, and May 5, 2023.</p><p><strong>Results: </strong>Qualitative findings highlighted nursing students' reliance on the web for educational materials, the significant role of mHealth applications in clinical skill development, and the emergence of social media platforms as community-building tools. Quantitative results revealed high-frequency web use (276/300, 92%) for educational purposes, regular mHealth application usage (204/300, 68%) in clinical settings, and active engagement on social media platforms (240/300, 80%). Traditional methods such as face-to-face interactions (216/300, 72%) and practical experiences (255/300, 85%) were preferred for developing essential skills.</p><p><strong>Conclusions: </strong>The study underscores nursing students' preference for an integrated approach, recognizing the complementary strengths of traditional and digital methods. Challenges include concerns about information accuracy and ethical considerations in digital spaces. The findings emphasize the need for curriculum adjustments that seamlessly integrate diverse communication modalities to create a dynamic learning environment. Educators play a crucial role in shaping this integration, emphasizing the enduring value of face-to-face interactions and practical experiences while harnessing the benefits of digital resources. Clear guidelines on professional behavior online are essential. Overall, the study expands the understanding of communication modalities among nursing students in Iraq and provides valuable insights for health care education stakeholders globally.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"7 ","pages":"e55744"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11611782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing AI Data Ethics in Nursing: Future Directions for Nursing Practice, Research, and Education.","authors":"Patricia A Ball Dunlap, Martin Michalowski","doi":"10.2196/62678","DOIUrl":"10.2196/62678","url":null,"abstract":"<p><strong>Unlabelled: </strong>The ethics of artificial intelligence (AI) are increasingly recognized due to concerns such as algorithmic bias, opacity, trust issues, data security, and fairness. Specifically, machine learning algorithms, central to AI technologies, are essential in striving for ethically sound systems that mimic human intelligence. These technologies rely heavily on data, which often remain obscured within complex systems and must be prioritized for ethical collection, processing, and usage. The significance of data ethics in achieving responsible AI was first highlighted in the broader context of health care and subsequently in nursing. This viewpoint explores the principles of data ethics, drawing on relevant frameworks and strategies identified through a formal literature review. These principles apply to real-world and synthetic data in AI and machine-learning contexts. Additionally, the data-centric AI paradigm is briefly examined, emphasizing its focus on data quality and the ethical development of AI solutions that integrate human-centered domain expertise. The ethical considerations specific to nursing are addressed, including 4 recommendations for future directions in nursing practice, research, and education and 2 hypothetical nurse-focused ethical case studies. The primary objectives are to position nurses to actively participate in AI and data ethics, thereby contributing to creating high-quality and relevant data for machine learning applications.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"7 ","pages":"e62678"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing artificial intelligence data ethics in nursing: future directions for nursing practice, research, and education.","authors":"Patricia A Ball Dunlap, Martin Michalowski","doi":"10.2196/62678","DOIUrl":"10.2196/62678","url":null,"abstract":"<p><strong>Unstructured: </strong>The ethics of artificial intelligence (AI) are increasingly recognized due to concerns such as algorithmic bias, opacity, trust issues, data security, and fairness. Specifically, machine learning algorithms, central to AI technologies, are essential in striving for ethically sound systems that mimic human intelligence. These technologies rely heavily on data, which often remain obscured within complex systems and must be prioritized for ethical collection, processing, and usage. The significance of data ethics in achieving responsible AI was first highlighted in the broader context of healthcare and subsequently in nursing. This presentation explores the principles of data ethics, drawing on relevant frameworks and strategies identified through a formal literature review. These principles apply to real-world and synthetic data in AI and machine learning contexts. Additionally, the data-centric AI paradigm is briefly examined, emphasizing its focus on data quality and the ethical development of AI solutions that integrate human-centered domain expertise. The ethical considerations specific to nursing are addressed, including four recommendations for future directions in nursing practice, research, and education and two hypothetical nurse-focused ethical case studies. The primary objectives are to position nurses to actively participate in AI and data ethics, thereby contributing to creating high-quality, relevant data for machine learning applications.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experiences of Using a Digital Guidance and Assessment Tool (the Technology-Optimized Practice Process in Nursing Application) During Clinical Practice in a Nursing Home: Focus Group Study Among Nursing Students.","authors":"Hege Mari Johnsen, Andréa Aparecida Gonçalves Nes, Kristine Haddeland","doi":"10.2196/48810","DOIUrl":"10.2196/48810","url":null,"abstract":"<p><strong>Background: </strong>Nursing students' learning during clinical practice is largely influenced by the quality of the guidance they receive from their nurse preceptors. Students that have attended placement in nursing home settings have called for more time with nurse preceptors and an opportunity for more help from the nurses for reflection and developing critical thinking skills. To strengthen students' guidance and assessment and enhance students' learning in the practice setting, it has also been recommended to improve the collaboration between faculties and nurse preceptors.</p><p><strong>Objective: </strong>This study explores first-year nursing students' experiences of using the Technology-Optimized Practice Process in Nursing (TOPP-N) application in 4 nursing homes in Norway. TOPP-N was developed to support guidance and assessment in clinical practice in nursing education.</p><p><strong>Methods: </strong>Four focus groups were conducted with 19 nursing students from 2 university campuses in Norway. The data collection and directed content analysis were based on DeLone and McLean's information system success model.</p><p><strong>Results: </strong>Some participants had difficulties learning to use the TOPP-N tool, particularly those who had not attended the 1-hour digital course. Furthermore, participants remarked that the content of the TOPP-N guidance module could be better adjusted to the current clinical placement, level of education, and individual achievements to be more usable. Despite this, most participants liked the TOPP-N application's concept. Using the TOPP-N mobile app for guidance and assessment was found to be very flexible. The frequency and ways of using the application varied among the participants. Most participants perceived that the use of TOPP-N facilitated awareness of learning objectives and enabled continuous reflection and feedback from nurse preceptors. However, the findings indicate that the TOPP-N application's perceived usefulness was highly dependent on the preparedness and use of the app among nurse preceptors (or absence thereof).</p><p><strong>Conclusions: </strong>This study offers information about critical success factors perceived by nursing students related to the use of the TOPP-N application. To develop similar learning management systems that are usable and efficient, developers should focus on personalizing the content, clarifying procedures for use, and enhancing the training and motivation of users, that is, students, nurse preceptors, and educators.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"7 ","pages":"e48810"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11422751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Student Perspectives and Experiences of Online Opportunities for Virtual Care Skills Development: Sequential Explanatory Mixed Methods Study.","authors":"Lorelli Nowell, Sara Dolan, Sonja Johnston, Michele Jacobsen, Diane Lorenzetti, Elizabeth Oddone Paolucci","doi":"10.2196/53777","DOIUrl":"10.2196/53777","url":null,"abstract":"<p><strong>Background: </strong>Caring profession students require skills and competencies to proficiently use information technologies for providing high-quality and effective care. However, there is a gap in exploring the perceptions and experiences of students in developing virtual care skills within online environments.</p><p><strong>Objective: </strong>This study aims to better understand caring professional students' online learning experiences with developing virtual care skills and competencies.</p><p><strong>Methods: </strong>A sequential explanatory mixed methods approach, integrating both a cross-sectional survey and individual interviews, was used to better understand caring professional students' online learning experiences with developing virtual care skills and competencies.</p><p><strong>Results: </strong>A total of 93 survey and 9 interview participants were drawn from various faculties, including students from education, nursing, medicine, and allied health. These participants identified the barriers, facilitators, principles, and skills related to learning about and delivering virtual care, including teaching methods and educational technologies.</p><p><strong>Conclusions: </strong>This study contributes to the growing body of educational research on virtual care skills by offering student insights and suggestions for improved teaching and learning strategies in caring professions' programs.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"7 ","pages":"e53777"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}