JMIR nursing最新文献

{"title":"Acceptance of Digital Technology Among Nursing Staff in Geriatric Long-Term Care: Systematic Review.","authors":"Jeton Iseni, Walter Swoboda, Daniel Houben, Roman Hilla","doi":"10.2196/82223","DOIUrl":"10.2196/82223","url":null,"abstract":"<p><strong>Background: </strong>Digital technologies are increasingly being introduced into the health care system and in settings such as hospitals and geriatric long-term care (LTC) facilities, offering potential benefits such as improved care quality, reduced workload, or enhanced documentation processes. However, the success of these technologies also depends on the acceptance by the primary users, that is, the nursing staff.</p><p><strong>Objective: </strong>This review synthesizes empirical studies that have explored the acceptance of digital technologies by nursing staff in geriatric LTC settings, building upon the foundational work by Yu et al (2009). The goal is to identify influencing factors, assess the extent of existing evidence, and highlight research gaps in this care setting.</p><p><strong>Methods: </strong>A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. The SPIDER (sample, phenomenon of interest, design, evaluation, research type) framework was used for eligibility criteria. Databases searched included PubMed, ACM Digital Library, Web of Science, and the Health Administration Database ProQuest. Studies were included if they empirically examined the acceptance of digital technologies by nursing staff in geriatric LTC settings. Two reviewers independently screened the studies, extracted data, and assessed methodological quality using the CASP (Critical Appraisal Skills Programme) checklist.</p><p><strong>Results: </strong>A total of 3 studies met the criteria, highlighting a gap in research on this topic. The studies applied cross-sectional quantitative designs and highlighted critical determinants of technology acceptance, including perceived usefulness, ease of use, digital competence, and organizational support. The studies involved a total of 1019 participants from Germany, Australia, and the Netherlands. Barriers included lack of user involvement, lack of training, poor system design, and demographic differences in digital affinity.</p><p><strong>Conclusions: </strong>This review shows that the acceptance of digital technologies by nursing staff in geriatric LTC settings is shaped by a constellation of individual factors, such as digital competence and perceived relevance of technology, as well as organizational factors such as access to training and involvement of staff in the implementation process. Despite these insights, the limited number of empirical studies highlights a research gap in this care setting. To ensure sustainable digital transformation in geriatric LTC, future research should prioritize rigorous and participatory approaches, using longitudinal, intervention-based, or multilevel study designs.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"9 ","pages":"e82223"},"PeriodicalIF":5.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12807401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986037","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":"Reducing Hospital-Acquired Pressure Injuries via a Quality Improvement Initiative: Results and Insights Gained.","authors":"Mousa Asiri, Homoud Alanazi, Homoud Alanazi, Sahar Abdulkarim Al-Ghareeb, Bander Mohammed Alanazi, Badr Ayed Alenazy","doi":"10.2196/90714","DOIUrl":"https://doi.org/10.2196/90714","url":null,"abstract":"<p><strong>Background: </strong>Hospital-acquired pressure injuries (HAPIs) remain a largely preventable cause of patient injury and are often utilized as nursing-sensitive quality metrics. At a tertiary military hospital in XXXXXX, rising HAPI rates necessitated implementing a comprehensive quality improvement program in accordance with the National Database of Nursing Quality Indicators (NDNQI) guidelines. On the basis of Donabedian's Structure-Process-Outcome model, we hypothesized that the implementation of a standardized, evidence-based pressure injury prevention bundle, accompanied by structured staff education (structure), will enhance adherence to prevention practices (process) and markedly decrease HAPI incidence and prevalence (outcomes) among hospitalized adult inpatients.</p><p><strong>Objective: </strong>To assess the effect of introducing a standardized, evidence-based pressure injury prevention bundle and corresponding staff education on HAPI incidence and prevalence.</p><p><strong>Methods: </strong>We implemented a comprehensive hospital-wide quality improvement project utilizing a pre-post methodology underpinned by Plan-Do-Study-Act (PDSA) cycles, statistical process control monitoring, and the FOCUS-PDSA framework. The strategy established a standardized preventive package for high-risk patients; it included routine risk and skin assessments, scheduled repositioning, pressure redistribution support surfaces, nutrition optimization with dietitian input, and moisture control. The primary outcomes were monthly HAPI incidence (per 1,000 patient-days), measured using wound care census and unit reporting, and quarterly HAPI prevalence, evaluated using NDNQI surveys by trained NDNQI link nurses, with >90% interrater reliability for staging.</p><p><strong>Results: </strong>In the initial deployment phase (July-December 2023), the HAPI incidence rate was 2.32 per 1,000 patient-days (267 cases/115,314 patient-days). The incidence declined to 1.44 per 1,000 patient-days (330 cases/229,647 patient-days) in 2024 (38% reduction from the baseline) and to 0.88 per 1,000 patient-days (98 cases/111,589 patient-days) by June 2025, (62% reduction from the baseline). The prevalence decreased from 5.12% in Q3 2023 to 1.38% in Q3 2024 and remained low at 1.43% in Q2 2025.</p><p><strong>Conclusions: </strong>Implementation of a standardized prevention bundle, supported by systematic staff education, interdisciplinary collaboration, and periodic incidence and prevalence surveillance was associated with sustained reductions in HAPI incidence and prevalence over 2 years. These findings support a bundle-based approach to prevention, combined with real-time feedback and competency-driven teaching, as a scalable means of enhancing patient safety.</p><p><strong>Clinicaltrial: </strong>none.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893531","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":"Acceptance of Digital Technology among Nursing Staff in Geriatric Long-Term Care: A Systematic Review.","authors":"Jeton Iseni, Walter Swoboda, Daniel Houben, Roman Hilla","doi":"10.2196/82223","DOIUrl":"https://doi.org/10.2196/82223","url":null,"abstract":"<p><strong>Background: </strong>Digital technologies are increasingly being introduced into the healthcare system and in settings like hospitals and geriatric long-term care (LTC) facilities, offering potential benefits such as improved care quality, reduced workload or enhanced documentation processes. However, the success of these technologies depends also on the acceptance by the primary users, the nursing staff.</p><p><strong>Objective: </strong>This review synthesizes empirical studies that have explored the acceptance of digital technologies by nursing staff in geriatric LTC settings, building upon the foundational work by Yu et al. (2009). The goal is to identify influencing factors, assess the extent of existing evidence and highlight research gaps in this care setting.</p><p><strong>Methods: </strong>A systematic literature review was conducted following PRISMA 2020 guidelines. The SPIDER framework was used for eligibility criteria. Databases searched included PubMed, ACM Digital Library, Web of Science and the Health Administration Database ProQuest. Studies were included if they empirically examined the acceptance of digital technologies by nursing staff in geriatric LTC settings. Two reviewers independently screened the studies, extracted data and assessed methodological quality using the CASP (Critical Appraisal Skills Programme) checklist.</p><p><strong>Results: </strong>A total of three studies met the criteria, highlighting a gap in research on this topic. The studies applied cross-sectional quantitative designs, highlighted critical determinants of technology acceptance, including perceived usefulness, ease of use, digital competence and organizational support. The studies involved a total of n=1,019 participants from Germany, Australia and The Netherlands. Barriers included lack of user involvement, lack of training, poor system design and demographic differences in digital affinity.</p><p><strong>Conclusions: </strong>This review shows that the acceptance of digital technologies by nursing staff in geriatric LTC settings is shaped by a constellation of individual factors, such as digital competence and perceived relevance of technology, as well as organizational factors like access to training and involvement of staff in the implementation process. Despite these insights, the limited number of empirical studies highlights a research gap in this care setting. To ensure sustainable digital transformation in geriatric LTC, future research should prioritize rigorous and participatory approaches, using longitudinal, intervention-based or multilevel study designs.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145851744","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":"Impact of COVID-19 Pandemic-Induced Changes in Clinical Practicums on the Mental Health of Newly Graduated Nurses: Longitudinal Study.","authors":"Takashi Ohue, Yuka Ohue","doi":"10.2196/79556","DOIUrl":"10.2196/79556","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic disrupted nursing education worldwide, particularly clinical practicums, reducing opportunities for hands-on learning. Newly graduated nurses have reported increased stress, reduced confidence, and a higher risk of burnout. However, few studies have examined the long-term mental health effects of these disruptions.</p><p><strong>Objective: </strong>This study aimed to longitudinally examine how changes in clinical practicums during the COVID-19 pandemic affected the mental health of nurses who graduated in the academic year 2021-2022.</p><p><strong>Methods: </strong>A quantitative longitudinal study was conducted at 3 time points: June 2022, September 2022, and December 2022. This study assessed the nurses' demographic data and the perceived impact on the students of disruptions in domain-specific and integration practicums, practicum formats, and clinical difficulty. Instruments used included the Nursing Job Stressor Scale, the Maslach Burnout Inventory, and adapted items for measuring intention to leave the profession. Participants were categorized into high- and low-impact groups. A 2-way ANOVA was used to examine mental health indicators over time.</p><p><strong>Results: </strong>Participants who perceived a greater impact of practicum disruptions reported substantially higher levels of clinical difficulty and stressors. In the September 2022 survey, those perceiving less impact from the integration practicum reported a stronger intention to continue nursing. In the December 2022 survey, emotional exhaustion, a core component of burnout, was significantly higher in the high-impact group.</p><p><strong>Conclusions: </strong>The perceived quality and extent of clinical practicum experiences significantly influenced the psychological burden and career intentions of newly graduated nurses. Disruptions caused by the COVID-19 pandemic may have lasting effects on nurses' mental health. These findings underscore the need for continuous workplace support and targeted mental health interventions for early-career nurses to ensure safe and sustainable nursing practice.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e79556"},"PeriodicalIF":5.0,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12724397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145812211","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":"Impact of AI Literacy on Well-Being Among Nursing Students-Mediating Roles of Empowerment and Anxiety: Cross-Sectional Study.","authors":"Amira Alshowkan, Emad Shdaifat","doi":"10.2196/79789","DOIUrl":"10.2196/79789","url":null,"abstract":"<p><strong>Background: </strong>The integration of artificial intelligence (AI) in health care is changing nursing practice, and it calls for the acquisition of AI literacy by students, which includes knowledge, skills, and attitudes. An understanding of the effect of AI literacy on the well-being and empowerment of students is crucial in guiding effective educational strategies.</p><p><strong>Objective: </strong>This study aims to investigate the impact of AI literacy on well-being, with psychological empowerment and anxiety serving as mediating variables. Using partial least squares structural equation modeling (PLS-SEM), this study examines gender differences within these relationships.</p><p><strong>Methods: </strong>A cross-sectional design was used, and data were gathered from 497 nursing students from Imam Abdulrahman Bin Faisal University, Saudi Arabia, via a structured online questionnaire assessing AI literacy, psychological empowerment, anxiety, and well-being. PLS-SEM was used to evaluate both the measurement and structural models, encompassing mediation and multigroup analyses based on gender.</p><p><strong>Results: </strong>The constructs demonstrated substantial reliability and validity, and the model's fit was deemed satisfactory. Well-being was moderately accounted for (R²=0.41), whereas empowerment and anxiety exhibited lower levels of explained variance. All hypotheses were supported, indicating that AI literacy positively influenced empowerment and negatively affected both anxiety and well-being. Furthermore, empowerment was found to negatively impact both anxiety and well-being. The mediation effects were significant, and no gender differences were observed.</p><p><strong>Conclusions: </strong>The study demonstrates that AI literacy significantly influences psychological empowerment, anxiety, and overall well-being through both direct and indirect pathways. The findings elucidate the intricate relationships among these variables and provide evidence for the applicability of the model across genders. This underscores the critical importance of promoting AI literacy and empowerment as a means to improve well-being outcomes.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e79789"},"PeriodicalIF":5.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12721487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145812226","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":"Sociotechnical Needs of Registered Nurses in the Heart Failure Hospitalizations of African American Patients: Cross-Sectional Study.","authors":"Tremaine Brueon Williams, Milan Bimali, Maryam Y Garza, Pearman Parker, Chase Paladino-Vaden, Emel Seker, Alisha Crump, Randy Rice, Latrina Prince, Taren Massey-Swindle, Kevin Wayne Sexton","doi":"10.2196/75080","DOIUrl":"10.2196/75080","url":null,"abstract":"<p><strong>Background: </strong>The African American population is disproportionately impacted by congestive heart failure (CHF). The impact includes a hospitalization rate that is 2.5 times higher and a hospital stay that is, on average, a quarter of a day longer compared with Caucasians. Notably, nursing care has been associated with nearly a 30% decrease in hospitalizations and readmissions. Previous studies have demonstrated that registered nurses (RNs), working in conjunction with electronic health record systems to conduct care tasks, may optimize length of stay in African Americans with CHF.</p><p><strong>Objective: </strong>This study aimed to identify the needs of RNs who performed sociotechnical tasks, the perceived importance of these sociotechnical tasks, and the perceived performance of these tasks by RNs, in relation to the length of stay of their African American patients with CHF.</p><p><strong>Methods: </strong>The study used an observational, cross-sectional survey design in RNs who were randomly selected from a total population of 3498 RNs who provided care to 22,703 African American patients with CHF within 113,543 heart failure hospitalizations between January 1, 2015, and January 1, 2024. The RNs were retrospectively stratified into 2 groups based on EHR data: those whose African American patients had a mean length of stay of 10 days or less (Group A) and those whose mean length of stay was greater than 10 days (Group B). Descriptive statistics, Cohen d, and a 2-sided unpaired t test were used to analyze the data.</p><p><strong>Results: </strong>The total sample of 200 RNs responded to the survey (100% survey completion rate). Group A (100 RNs) reported the least important task as drawing conclusions about how to use the EHR to care for African American patients (mean 4.66, SD 1.82). The least important task in Group B (100 RNs) was reading published research on African American patients (mean 4.88, SD 1.70). Group A reported performing best in caring for African American patients (mean 5.61, SD 1.44). Group B reported performing best at caring for all patients (mean 5.86, SD 1.04). A total of 17 significant sociotechnical needs were identified among groups. In total, 2 sociotechnical needs were unique to group B: caring for patients (ie, the full scope of social and technological processes in nursing care; Cohen d=0.32, 95% CI 0.04-0.59; P=.04) and working with information related to a patient's CHF in the EHR (eg, laboratory results, discharge summaries, or radiographic images) to care for the patient (Cohen d=0.33, 95% CI 0.05-0.61; P=.03).</p><p><strong>Conclusions: </strong>Lengths of patient stay may be reduced by identifying and addressing sociotechnical needs through targeted training, nursing care interventions, and RN-led risk stratification guidelines for working with EHRs to reduce lengths of stay in those who are disproportionately impacted by CHF.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e75080"},"PeriodicalIF":4.0,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12700336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745961","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":"An Expert Knowledge Algorithm and Model Predicting Wound Healing Trends for a Decision Support System for Pressure Injury Management in Home Care Nursing: Development and Validation Study.","authors":"Aya Kitamura, Aruto Ando, Gojiro Nakagami, Hiromi Sanada","doi":"10.2196/65716","DOIUrl":"10.2196/65716","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Home-visiting nurses have difficulty selecting appropriate pressure injury (PI) management despite using clinical practice guidelines in various home-visiting settings. Clinical decision support systems can help home-visiting nurses' decision-making.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop a care algorithm reflecting the expertise of a wound expert nurse and a predictive model for the change of PI severity to inform home-visiting nurses to receive actual consultation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;First, an existing algorithm was modified by semistructured interviews with a certified wound expert nurse. Case information was input into both base and high-expertise algorithms, which provided care recommendations across 9 fields: (1) pressure relief; (2) nutritional management; (3) shear relief; (4) moisture management; (5) wound dressing use; (6) care for physical factors including bone prominence, obesity, joint contractures, and periwound edema; (7) care for systemic disorder; (8) selection of wound dressings, ointments, and negative pressure wound therapy; and (9) wound cleansing. An expert interviewee assessed the high-expertise algorithm's recommendations on a 5-point scale, comparing them to the base algorithm and their own clinical judgment. To measure the algorithm's applicability, agreement proportions were calculated as the number of vignettes where the care recommendation was considered appropriate or total number of vignettes. To measure the algorithm's alignment, improvement proportions were calculated as the number of vignettes where the care recommendation improved or total number of vignettes excluding vignettes when the existing and high-expertise algorithm both showed an appropriate recommendation. Expected healing levels were evaluated by a 4-point scale where 4 indicates the high-expertise algorithm can \"much improve\" the case. Second, predictive distributions of changes in DESIGN-R 2020 score, PI severity score, were estimated with a hierarchical Bayesian model. The best model determined using training data (n=42) calculated coverage probabilities of 90% prediction interval in test data (n=34). The coverage probability of a 90% prediction interval was defined as follows: the number of times when actual scores were within the 90% prediction interval or the number of assessments when the prediction was conducted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The agreement proportions were 0.92 (33/36), 0.75 (27/36), and 0.89 (32/36) for each round. The improvement proportions were 0.73 (8/11), 0.25 (3/12), and 0.76 (13/17), respectively. The expected healing level was 2.67, 3.00, and 3.25, respectively. Coverage probabilities of 90% prediction interval in the test data were 0.67 (4/6), 0.83 (5/6), 0.86 (6/7), and 0.80 (8/10), respectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study developed an algorithm reflecting the expertise and a model to estimate predictive distributions of chan","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e65716"},"PeriodicalIF":4.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12688022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145716893","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 Health Professionals' Knowledge and Perceptions of Telehealth Implementation in Multisite Public Hospitals of East Wollega Zone, Oromia, West Ethiopia: Cross-Sectional Study.","authors":"Adugna Oluma, Tesfaye Abera Gudeta, Misganu Diriba Olana, Aliyi Benti Daba, Demiso Geneti Dinagde, Ashenafi Tesfaye Yadesa, Lammi Atomsa, Takele Mitiku Tesema","doi":"10.2196/71870","DOIUrl":"10.2196/71870","url":null,"abstract":"<p><strong>Background: </strong>A significant gap exists among health professionals regarding telehealth due to limited knowledge and varying perceptions. This disparity is particularly pronounced in low-income regions, where numerous barriers hinder its implementation.</p><p><strong>Objective: </strong>This study aimed to assess the knowledge and perception of telehealth among health professionals in public hospitals in East Wollega, Oromia Region, Ethiopia.</p><p><strong>Methods: </strong>A study was conducted with 397 health professionals in public health facilities in East Wollega Zone, selected through systematic random sampling. Data were collected via self-administered questionnaires from March 2024 to June 2024 and entered into EpiData (version 3.1; EpiData Association) and analyzed using SPSS (version 24; IBM Corp). Descriptive statistics, including percentages and frequency distributions, were used. The Hosmer and Lemeshow test assessed variable homogeneity. Significant variables (P<.05) in bivariable logistic regression analysis were included in multivariable logistic analysis and reported as adjusted odds ratios (AOR) with 95% CI.</p><p><strong>Results: </strong>Findings indicated high levels of knowledge and perception among participants. Factors significantly associated with knowledge included educational status (AOR 4.22, 95% CI 3.45-13.12; P=.001), income between Ethiopian Birr 7001-9000 (a currency exchange rate of Ethiopian Birr 52=US $1 during the study period; AOR 3.255, 95% CI 1.790-8.878; P=.001), access to computers (AOR 2.414, 95% CI 1.046-14.764; P=.001), consultation sessions (AOR 2.389, 95% CI 1.961-10.158; P=.001), and smartphone use (AOR 3.027, 95% CI 2.797-14.729; P=.001). For perception, significant associations included income over 9000 Birr (AOR 2.675, 95% CI 2.271-19.277; P=.001), improving quality of life (AOR 1.786, 95% CI 1.575-22.587; P=.009), increased accessibility (AOR 1.244, 95% CI 1.061-11.333; P=.04), consultation sessions (AOR 4.777, 95% CI 2.318-15.062; P=.001), and smartphone use (AOR 3.836, 95% CI 2.900-13.573; P=.001).</p><p><strong>Conclusions: </strong>Approximately two-thirds of participants demonstrated good knowledge and perception of telehealth. It is highly recommended to create awareness through educational programs for individuals with lower educational backgrounds and to improve access to technological devices.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e71870"},"PeriodicalIF":4.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12677981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679726","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":"Shift-to-Shift Information Transfer: Phenomenological Study of Nurses' Experiences.","authors":"María-Josefa Montoya-Garrido, Claudio-Alberto Rodríguez-Suárez, Noa Mateos-López, Yeray-Tomás Santiago-Díaz, Héctor González-de la Torre","doi":"10.2196/81703","DOIUrl":"10.2196/81703","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Handovers represent a critical moment for patient safety, where the effective transfer of information between nurses is essential. In this context, digital documentation systems such as identification, diagnosis, evolution, activities, support (IDEAS) have been implemented to standardize and enhance the quality of clinical handovers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to explore nurses' perceptions in the hospital setting regarding information transfer during shift changes. Specific objectives included identifying the perceived strengths and weaknesses of the handover process, as well as the difficulties and improvement proposals reported by nurses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A qualitative study with a phenomenological approach was conducted. Semistructured interviews were carried out with nurses from the Hospital Universitario Insular de Gran Canaria who had experience using the IDEAS system, between June 2023 and September 2024, until data saturation was reached. After transcribing the interviews, an inductive thematic analysis was performed to identify emerging themes using both descriptive and interpretative approaches. Axial coding through co-occurrence analysis, analytical triangulation, and reflexivity strategies was incorporated to strengthen the credibility and consistency of the findings. Atlas.ti software (version 25.0.1; Scientific Software Development GmbH) was used for the analysis. The study was approved by the local ethics committee (code: 2023-244-1).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;From the interviews (n=15), 6 subthemes were identified and grouped into three main themes: (1) nurses (difficulties and improvement proposals in information transfer, strengths and weaknesses in the handover process), (2) patients (electronic health records: Benefit for patients, transfer of patient information), and (3) records (Comments on the form, information management). Participants valued the structured access to clinical information provided by the IDEAS system. However, they reported limitations such as poor data prioritization, editing difficulties, outdated information, and a lack of integration between nursing and medical records. In addition, training deficiencies and variability in system use, particularly among less experienced professionals, were noted. Suggestions for improvement included redesigning the handover form, automating updates, incorporating brief clinical summaries, and providing ongoing training.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While the IDEAS system represents an improvement over previous handover methods, its effectiveness remains constrained by technical, organizational, and cultural barriers. Optimizing the system requires clinically oriented redesigns, alongside training strategies and an institutional culture that promotes shared responsibility for documentation quality. These elements are essential for establishing a safer, more standardized, and patient-cent","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e81703"},"PeriodicalIF":5.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12674989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672911","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":"Interpretation of Health-Smart Home Data and Implications for Clinical Decision-Making: Inductive Content Analysis.","authors":"Gordana Dermody, Diane J Cook, Roschelle L Fritz","doi":"10.2196/75234","DOIUrl":"10.2196/75234","url":null,"abstract":"<p><strong>Background: </strong>Health-smart home technologies offer real-time sensor-based monitoring of older adult activities of daily living, allowing for early detection of changes in health. The way clinicians interpret and use this data, particularly in visualized formats, such as bar, line, and pie graphs, remains underexplored.</p><p><strong>Objective: </strong>A qualitative descriptive study design with a quantitative component was used to explore how nurses interpret sensor-derived health data from health-smart homes in 3 cases.</p><p><strong>Methods: </strong>Using an inductive content analysis approach, we analyzed nurses' qualitative interpretations of existing sensor-derived health data from health-smart homes from 3 older adults living with ambient whole-home sensing. Nurses provided structured written feedback on visualized trends in sensor-derived health data, including activity, sleep, and mobility patterns.</p><p><strong>Results: </strong>The findings highlight both opportunities and challenges of using sensor-derived health data in older adults' care. Nurses identified key patterns in sleep, mobility, and home engagement, but interpretation difficulties, such as unclear sleep metrics and lack of clinical context, hindered decision-making. Nurses preferred bar and line graphs over pie charts for interpreting these data. Survey results show a statistically significant difference in how nurses rated different graph types (χ²2=17.1, P<.001), with pie charts rated significantly lower than both bar and line graphs (P<.001 and P=.008, respectively). These findings underscore the need for improved data visualization and integration to enhance the clinical utility of sensor-derived health data from health-smart homes.</p><p><strong>Conclusions: </strong>Findings indicate that nurses were able to provide accurate interpretations of the sensor-derived health data from health-smart homes. However, there is a need for improved visualization techniques and clinician training to optimize health-smart home data for early intervention. Standardized approaches to data representation could enhance nurses' ability to detect and act on subtle yet important information about older adults' health changes occurring in home settings.</p>","PeriodicalId":73556,"journal":{"name":"JMIR nursing","volume":"8 ","pages":"e75234"},"PeriodicalIF":5.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12638034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575027","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}