https://nursing.jmir.org/issue/feed 2024-02-16T10:45:27-05:00 JMIR Publications Inc. editor@jmir.org Open Journal Systems Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in JMIR Nursing...") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on https://nursing.jmir.org/, as well as this copyright and license information must be included. Virtualizing care from hospital to community: Mobile health, telehealth, and digital patient care. https://nursing.jmir.org/2026/1/e79188 2026-02-02T15:15:06-05:00 Marguerite Daus Brigid Connelly Drew Carter Heather M Gilmartin

Background: Leadership development programs in healthcare often fail due to their lack of adaptability to the schedules of busy clinicians. This study addresses the need for scalable, flexible programs tailored to nurse leaders. Objective: This case study evaluated the acceptability, appropriateness, and feasibility of the Relational Playbook, an evidence-based leadership development program developed in the Veterans Health Administration, delivered through the Whistle Systems employee recognition web and mobile applications. Methods: A one-year case study approach was deployed using descriptive survey data and qualitative interview analysis. The Playbook’s educational content and interventions were hosted on the Whistle platform, which integrates behavioral science and gamification strategies. Content was delivered weekly via app-based nudge notifications and email. Engagement metrics included activity completion rates. User experience data were collected through weekly reflection surveys (with Likert scale responses and open-text options), monthly check-ins, and a post-implementation acceptability, appropriateness, and feasibility survey and interview. Descriptive statistics summarized engagement levels and trends, while qualitative data were analyzed using content analysis to identify recurring concepts. Quantitative and qualitative data were analyzed sequentially for comprehensive insights. Results: Five cardiology nurse practitioners (NPs) from a large academic medical center, providing both inpatient and outpatient care, participated. The Whistle platform was deemed an acceptable, appropriate and feasible technology for delivering the Playbook content. Participants valued the weekly nudges, microlearning content, and flexibility of web and mobile applications. The Playbook content supported personal growth and fostered positive shifts in attitudes toward work. Conclusions: Delivering leadership development content through the Whistle platform is an acceptable approach to support the growth and well-being of busy nurse leaders.

2026-02-02T15:15:06-05:00 https://nursing.jmir.org/2026/1/e74942 2026-01-23T16:00:14-05:00 Lauren Bailey Lyndsay Howitt Nafsin Nizum Christine Buchanan Maureen Charlebois Jennifer Yoon RNAO Expert Panel

Background: Digital health refers to the field of knowledge and practice associated with the development and use of digital technologies to improve clinical practice and health outcomes. Knowledge of digital health technology is becoming essential for all nurses and health providers. Objective: The aim of this paper is to present the results of the systematic reviews that were used to inform the recommendations in a best practice guideline (BPG) following the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Reviews focused on digital health education for nurses and health providers, peer champion models, and use of predictive analytics in digital health environments. Methods: The BPG team in collaboration with a panel of 17 experts conducted five systematic reviews to address five recommendation questions. Systematic searches looked for relevant studies published in English from January 2017 to July 2022 from 10 databases. The GRADE approach was used to synthesize and evaluate the quality of evidence, ensuring the guideline aligned with international reporting standards. Results: Eighteen articles across four systematic reviews met the inclusion criteria. From these reviews, four corresponding recommendations were drafted for nurses and health providers. The strength of the recommendations was determined through discussion and consensus by the expert panel using the GRADE approach. One systematic review resulted in no recommendation due to insufficient evidence. Conclusions: The BPG on digital health provides four evidence-based recommendations for nurses and health providers on how to incorporate digital health technologies into clinical practice. This BPG is intended to be used across all health-care settings.

2026-01-23T16:00:14-05:00 https://nursing.jmir.org/2026/1/e78560 2026-01-23T15:45:07-05:00 Mary Henderson Betkus Davina Banner Leanne Currie Piper Jackson Shannon Freeman

Background: Artificial intelligence (AI) continues to expand into nursing and healthcare. Many examples of AI applications driven by machine or deep learning are in use already. Examples include wearable devices or automated alerts for risk prediction. AI tends to be promoted by non nurses, creating a risk that AI is not designed to best serve registered nurses who will be expected to use AI outcomes in practice. Community Health Nurses (CHNs) are a small but essential group providing health care in the community. CHNs’ familiarity with AI and their perceptions about its effect on their practice is unknown. Objective: The research aims to understand CHNs’ awareness, knowledge, and perceptions of AI on practice and gain insights to better involve them in AI. Methods: An online cross-sectional Canadian survey targeting CHNs was conducted April–July 2023. Descriptive statistics summarized respondents’ characteristics and perceptions of AI, followed by Chi-square test used to determine a relationship between respondents’ level of AI knowledge and their AI perceptions with odds ratio [OR] to determine strength of association. Results: A total of 228 CHNs participated with varying response rates per question. Most respondents were female (172/188, 91.5%), average age 45.5 years (178, SD 11.7) and average 13.5 years (176, SD 10.1) of community practice experience. Most respondents (205/228, 89.9%) felt they welcomed technology into their practice. They reported their understanding of AI technologies as ‘Good’ (95/220, 43.2%) and ‘Not Good’ (125/220, 56.8%). Overall, 39.6% (80/202) respondents felt uncomfortable with the development of AI. They agreed that: AI should be part of education (143/203, 70.4%), professional development (152/202, 75.2%) and that they should be consulted (195/203, 96.1%). Many respondents had concerns related to professional accountability if they accepted a wrong AI recommendation (157/202, 77.7%) or if they dismissed a correct AI recommendation (149/202, 73.8%). Respondents with ‘Good’ AI knowledge were significantly associated with, and twice as likely to indicate nursing will be revolutionized (P=.007; OR 2.28, 95% CI 1.25-4.18), nursing will be more exciting (P=.001, OR 2.52, 95% CI 1.42-4.47), healthcare will be more exciting (P=.004, OR 2.3, 95% CI 1.30-4.06), and agreed that AI is part of nursing (P=.01, OR 2.1, CI 1.19-3.68). Respondents with ‘Not Good’ AI knowledge were significantly associated with, and more likely to feel uncomfortable with AI developments (χ21=4.2, P=.04, OR 1.84, 95% CI 1.03-3.3). Conclusions: CHNs reporting ‘Good’ AI knowledge had more favorable perceptions towards AI. Overall, CHNs had professional concerns about accepting or dismissing AI recommendations. Potential solutions include educational resources to ensure that CHNs have a sound basis for AI in their practice, which would promote their comfort with AI. Further research should explore how CHNs could be better involved in all aspects of AI introduced into their practice.

2026-01-23T15:45:07-05:00 https://nursing.jmir.org/2026/1/e84106 2026-01-23T12:30:08-05:00 Channon Smith Keitumetse-Kabelo Murray Natasha Croome

Background: The demanding work environment of nurses in medical and emergency units often results in high stress, job dissatisfaction, and burnout. Adequate rest is crucial for maintaining nurses' physical health, mental clarity, and emotional resilience, yet it is often overlooked in these high-pressure settings. This qualitative study explores the perceptions of nurses at a hospital in Western Jamaica regarding the quality and duration of rest they receive and its impact on their professional, mental, physical, and personal well-being. The hospital was selected due to the unique challenges healthcare workers face in Jamaica, including limited resources, high patient loads, and frequent staff shortages, which may exacerbate rest-related issues. Objective: This study aimed to explore the perceptions of registered nurses working in the emergency and medical units of the hospital in Western Jamaica regarding their rest experience and its implications for burnout, job satisfaction, and overall health. Methods: The study employed a constructivist epistemological lens and utilized purposive sampling to select 12 registered nurses. The principal researcher conducted in-depth interviews with each participant via Zoom, using a semi-structured guide. Interviews lasted 25 to 45 minutes, were audio-recorded, and attended only by participants and the researcher. Thematic analysis was used to transcribe, code, and analyze the data, culminating in the development of a thematic map of findings. Results: The findings indicated that nurses face significant challenges in obtaining adequate rest due to staff shortages, heavy workloads, irregular shifts, and limited management support. Three primary themes emerged: [1] non-compliance with rest policies, [2] resource limitations, and [3] management issues, each influencing job satisfaction, burnout, and overall health. Within non-compliance, nurses highlighted suboptimal nurse-to-patient ratios, absenteeism, and inadequate break time. For example, ratios as high as “30 to 2” or “60 to 3” were cited, affecting nurses’ ability to take breaks. Resource constraints included inadequate staffing, insufficient staff replacement, and the absence of suitable rest areas. Management concerns included weak policy enforcement, inadequate policy awareness, and limited support for rest breaks. These challenges collectively contributed to poor sleep quality, increased stress, and diminished job satisfaction. Conclusions: The study highlights the need for systemic improvements to address nurse rest and well-being, including increased staffing, structured policies on break enforcement, and enhanced management engagement. While the study is specific to the hospital in Western Jamaica, the findings may have broader implications for healthcare systems in similarly resource-constrained settings in the Caribbean and other developing regions.

2026-01-23T12:30:08-05:00 https://nursing.jmir.org/2026/1/e78395 2026-01-21T14:30:08-05:00 Magdalena Vogt Sebastian Müller Glorianna Wagner-Jagfeld Renate Ranegger Sabin Zürcher Janine Vetsch

Background: Evidence-based practice is essential for delivering safe, high-quality nursing care, yet its implementation remains challenging due to barriers such as limited knowledge, lack of supportive organizational culture, and insufficient access to relevant knowledge at the point of care. Knowledge Management Systems (KMS) have the potential to bridge this gap by integrating evidence into the nursing process through technological support. Despite growing interest, the integration of KMS into daily nursing practice is still underexplored, especially from the perspective of frontline nurses. Objective: The aim of this study was to explore nurses’ perspectives on the requirements for a KMS that supports evidence-based practice at the point of care, with a focus on usability, process-integration into the electronic nursing care plan and patient chart, and implementation challenges and benefits. Methods: A qualitative study was conducted in a Swiss hospital using observations, focus-groups and individual interviews with six registered nurses, nine advanced practice nurses, two nursing managers and one head physician. Data were analyzed using thematic analysis. Results: The analysis revealed four main- and 10 subcategories, including the following: (1) Content of KMS, (2) Personal and structural factors of knowledge management, (3) Technical conditions of KMS, and (4) Implementation of a KMS. Participants emphasized the need for an intuitively structured, process-integrated system that links evidence-based information directly to the nursing interventions in the electronic nursing care plan and patient chart. Organizational support, interprofessional collaboration, and clear responsibilities were identified as critical for successful implementation. Conclusions: There is a clear need for a KMS that is user-friendly, seamlessly integrated into clinical workflows, and supports quick, reliable access to evidence-based knowledge. A KMS could enhance nurses’ access to reliable knowledge, promote evidence-based decision-making, and strengthen professional confidence at the point of care. By embedding evidence directly into the electronic nursing care plan and patient chart, such systems can streamline workflows, reduce time spent searching for information, and support more consistent application of best practices. These capabilities may improve information retrieval and contribute to safer, more consistent nursing practice.

2026-01-21T14:30:08-05:00 https://nursing.jmir.org/2026/1/e85649 2026-01-16T13:30:11-05:00 Habib Alrashedi Saad M Alderaan Nader Alnomasy Hamdi Lamine Khalil A Saleh Sameer A Alkubati

Background: Background: Assessing the current landscape of nurses' knowledge and attitudes is a critical first step in facilitating a smooth and effective transition towards AI-enhanced critical care. Objective: Objectives: We aimed in this study to assess the levels of and factors affecting the knowledge and general attitudes of critical care nurses towards artificial intelligence (AI). Methods: Methods: A cross-sectional correlational design was used. Data were collected using the Nurses' AI Knowledge Questionnaire and the 20-item General Attitudes toward AI Scale from May to July 2025. Using multivariate linear regression analysis, the significant factors affecting CCNs’ knowledge and attitudes were identified. Correlation between variables was assessed using Pearson's correlation coefficient. P-value was set at less than .05. Results: Results: The mean scores for CCNs’ knowledge and attitude towards AI were 4.93 ± 1.78 and 64.39 ± 8.26, respectively, indicating a moderate level of knowledge and a positive attitude towards AI. CCNs’ knowledge of AI was positively and significantly correlated with their attitude towards AI (r = .450, p < .001). Nurses aged 30–39 years (β = –.804, p = .017) and those aged 40 years or older (β = –1.285, p = .003) had lower knowledge scores than those aged 20–29 years. Similarly, female nurses reported significantly lower knowledge scores than their male counterparts (β = −.697, p = .007). In contrast, nurses with more than 5 years of experience had significantly higher knowledge levels (β = 1.203, p < .001). The model explained 19.4% of the variance in knowledge (Adjusted R² = .169, p < .001). Regarding attitudes, nurses aged 30–39 years (β = –4.806, p = .001) and those aged 40 years or older (β = –8.969, p < .001) reported less positive attitudes than those aged 20–29 years. Female nurses had significantly less positive attitudes than male nurses (β = –2.649, p = .015). Conversely, nurses with a master’s degree (β = 3.381, p = .002) and those with more than 5 years of experience (β = 8.084, p < .001) demonstrated more positive attitudes. The model explained 33.2% of the variance in attitude (Adjusted R² = .311, p < .001). Conclusions: Conclusion: CCNs in Hail City demonstrated moderate knowledge and positive attitudes toward AI, with knowledge positively correlated with attitudes. Age, sex, education, and clinical experience significantly influenced these perceptions. These findings highlight that the successful integration of AI into critical care depends on enhancing nurses’ literacy, confidence, and engagement through continuous, inclusive education, and involvement in AI development processes. Strengthening these aspects will facilitate effective AI adoption and support Saudi Arabia’s Vision 2030 for a technology-driven health care system. Clinical Trial: NA

2026-01-16T13:30:11-05:00 https://nursing.jmir.org/2026/1/e82223 2026-01-15T16:00:14-05:00 Jeton Iseni Walter Swoboda Daniel Houben Roman Hilla

Background: 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 their acceptance by the primary users, the nursing staff, especially in the field of geriatric LTC, where the care employees are more involved in basic care of the elderly. Objective: This work synthesizes subsequent 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. which was published in 2009 [1]. The goal is to highlight the extent to which this topic has been addressed in the literature, to identify influencing factors that have emerged in the literature over the past years regarding the acceptance of digital technologies in geriatric LTC facilities and to highlight the lack of empirical studies in the LTC-field. Methods: 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. Results: A total of three studies met the criteria, highlighting a gap in research on this topic. Although limited in number, these studies offer initial insights and provide a basis for further investigation. 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. Conclusions: This review underscores the insufficient scientific attention to digital technology acceptance among LTC nursing staff. Nevertheless, the results show that the acceptance of digital technologies by nursing staff in geriatric LTC settings is triggered by a constellation of individual factors, like digital competence and perceived relevance of technology and organizational factors like access to training and involvement of staff in the implementation process. However, to ensure sustainable digital transformation in geriatric LTC, further scientific evaluations to this specific care setting are urgently needed.

2026-01-15T16:00:14-05:00 https://nursing.jmir.org/2025/1/e79556 2025-12-23T10:30:07-05:00 Takashi Ohue Yuka Ohue

Background: 　The COVID-19 pandemic disrupted nursing education globally, 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. Objective: 　This study aimed to longitudinally examine how changes in clinical practicum during the COVID-19 pandemic affected the mental health of nurses who graduated in the 2021–22 academic year. Methods: 　A quantitative longitudinal study was conducted at three points: June, September, and December 2022. Demographic data, perceived impact of domain-specific and integration practicums, practicum formats, and clinical difficulty were assessed. Instruments used included the Nursing Job Stressor Scale (NJSS), Maslach Burnout Inventory (MBI), and items adapted from Tsuchie et al. to measure intention to leave. Participants were categorized into high- and low-impact groups, and two-way ANOVA was used to examine mental health indicators over time. Results: 　Participants who perceived a greater impact from practicum disruptions reported significantly higher levels of clinical difficulty and stressors. In the December survey, “Emotional exhaustion, a core component of burnout, was significantly higher in the high-impact group. “Additionally, in September, those perceiving less impact from the integration practicum reported a stronger intention to continue nursing. Conclusions: 　The results suggest that the perceived quality and extent of clinical practicum experiences significantly influence the psychological burden and career intentions of newly graduated nurses. Disruptions caused by the COVID-19 pandemic may have lasting effects on nurse mental health. These findings underscore the need for continuous workplace support and targeted mental health interventions for early-career nurses to ensure safe, sustainable nursing practice.

2025-12-23T10:30:07-05:00 https://nursing.jmir.org/2025/1/e79789 2025-12-22T15:00:12-05:00 Amira Alshowkan Emad Shdaifat

Background: The integration of artificial intelligence (AI) in healthcare 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. Objective: This study aims to investigate the impact of AI literacy on well-being, with psychological empowerment and anxiety serving as mediating variables. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the study examines gender differences within these relationships. Methods: A cross-sectional design was utilized, and data were gathered from university-level students via a structured online questionnaire assessing AI literacy, psychological empowerment, anxiety, and well-being. PLS-SEM was employed to evaluate both the measurement and structural models, encompassing mediation and multi-group analyses based on gender. Results: The constructs demonstrated substantial reliability and validity, and the model 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. Conclusions: 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. Clinical Trial: N/A

2025-12-22T15:00:12-05:00 https://nursing.jmir.org/2025/1/e75080 2025-12-12T14:15:06-05:00 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

Background: African Americans are disproportionately impacted by congestive heart failure (CHF). The impact includes a two and a half times greater hospitalization rate and a fourth of a day longer length of hospitalization than Caucasians, of which nursing care has been associated with nearly a 30% decrease in hospitalizations and readmissions. Prior studies have demonstrated that registered nurses (RNs), working in conjunction with electronic health record systems (EHRs) to conduct care tasks, may optimize length of stay in African Americans with CHF. Objective: The objective of the study was to identify the needs of RNs who performed socio-technical tasks, the perceived importance of these socio-technical tasks, and the perceived performance of these tasks by RNs, in relation to the length of stay of their African American patients with CHF. Methods: The study employed an observational, cross-sectional survey design in RNs who were randomly selected from a total population of 3,498 RNs who provided care to 22,703 African Americans with CHF within 113,543 heart failure hospitalizations between January 1, 2015, and January 1, 2024. The RNs were retrospectively stratified into two 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’s d, and a two-sided unpaired t-test were used to analyze the data. Results: 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 Americans (Mean=4.66, SD=1.82). The least important task in Group B (100 RNs) was reading published research on African Americans (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 seventeen significant socio-technical needs were identified among groups. Two socio-technical needs were unique to Group B: caring for patients (i.e., the full scope of social and technological processes in nursing care) (Cohen’s d=0.32, 95% CI: 0.04,0.59, P=.04) and working with information related to a patient's CHF in the EHR (e.g., laboratory results, discharge summaries, or radiographic images) to care for the patient (Cohen’s d=0.33, 95% CI: 0.05,0.61, P=.03). Conclusions: Lengths of patient stay may be reduced by identifying and addressing socio-technical 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. Clinical Trial: N/a

2025-12-12T14:15:06-05:00