Nursing Administration Quarterly最新文献

The Association of California Nurse Leaders's (ACNL) Recognition and Scholarship Committee (RSC) investigated the role of ACNL in providing scholarships to advance nursing education and professional development. The committee played a crucial role in overseeing the selection process for scholarship recipients to ensure that applicants were evaluated fairly. The RSC was responsible for meeting the committee's goals and the mission of ACNL. In this review, a description of how scholarships supported Registered Nurses in overcoming financial barriers to further education and enhance professional growth by positively impacting nursing practice and potentially patient outcomes was investigated. The RSC oversees the selection and evaluation process based on specific criteria, such as Grade Point Average, personal statements including the need for scholarship, and letters of recommendation. The committee also highlighted the significant impact of ACNL scholarships through a survey conducted among 10 recipients from 2022 to 2025, showing a 100% response rate and substantial professional growth by the recipients. Seventy percent of the recipients presented at major health and nursing conferences; 60% completed their advanced degree; and 50% of scholarship recipients were promoted since receiving their scholarship award.

Despite significant concerns about the broad use of artificial intelligence (AI) in clinical practice, registered nurses are commonly challenged to adopt, evaluate, and improve these digital solutions in almost any care environment. The aim of this study is to appraise, then analyze, the scientific literature for contemporary digital innovations that improve nursing practice through the use of AI. This scoping review with thematic analysis of digital innovations impacting nursing practice was conducted using CINAHL and PubMed databases (2022-2024). We describe the use of large language models and associated software tools in supporting this work, highlighting the essential human-in-the-loop activities involved. Results: This review highlights emerging themes, notably improved patient safety and greater trust and acceptance. A persistent emphasis on AI as an assistive entity was found across many products, processes and pilots identified in our corpus. This body of literature is rapidly evolving and characterized by multiple competing themes. The proper implementation and execution of AI as an assistive mechanism is crucial for safety enhancement, improved quality, and trust. Recommendations include the provision of transparency in all aspects of AI innovations for practice (from inception to evaluation) and participatory implementation activities.

Although many articles address retention, few define the concept. There is a need for all health care organizations to adopt a standard definition in order to make accurate comparisons. This organization provides a definition of retention, measures pre-COVID and post-COVID retention, and uses psychometrically tested tools to measure the effectiveness of an accredited Nurse Residency Program. Characteristics of nurses who stay and nurses who leave are provided. Evidence-based retention strategies and opportunities for improvement are discussed, as all health care organizations and nurse leaders desire to have a return on their investment of enculturating newly licensed nurses.

Therapeutic communication is an essential nursing skill to foster nurse-patient relationships. Prelicensure nursing students experience anxiety when communicating with patients, which can impact performance. Generative artificial intelligence is a new method of simulation that offers an opportunity for nursing students to practice therapeutic communication in a safe environment. The purposes of this study were to explore: (1) the effect of generative artificial intelligence simulation on first-year baccalaureate nursing students' therapeutic communication skill; and (2) first-year baccalaureate nursing students' perceptions of using generative artificial intelligence simulation to practice therapeutic communication. Participants in the experimental group engaged in 2 virtual generative artificial intelligence simulation scenarios focused on therapeutic communication through the SimConverse platform. Findings from this study revealed nonstatistically significant higher therapeutic communication skill among students who engaged in generative AI simulation versus those who did not. Qualitative data revealed 3 themes: (1) realism, (2) practice without pressure, and (3) feedback. Given the limited, conflicting empirical findings available related to generative AI simulation and therapeutic communication among prelicensure nursing students, additional research is needed in this area. Moreover, nursing faculty need instruction on artificial intelligence to adequately train generative artificial intelligence models for appropriate use in nursing education.

Background: The objective of this paper was to systematically evaluate and explore what is currently known about the use of virtual reality (VR) as a patient education instrument to convey medical information and how conveying this health information using VR ultimately influences patient outcomes.

Methods: In this literature review, relevant studies were identified. Study characteristics (eg, setting, study design, sample characteristics, VR methods, and outcomes), and findings were extracted revealing diverse applications of VR in patient education, demonstrating efficacy in areas such as preventive care, discharge education, symptom management, and preoperative preparation. Our findings revealed several potential areas for implementation.

Results: The results of the review reveal that VR has positive effects on patient education including improvement in knowledge, self-efficacy, psychological outcomes, and physiological outcomes. These findings provide guidance for improving patient education using VR.

Conclusion: The effectiveness of patient education utilizing VR is critical to optimal health outcomes, and VR can augment this process. Health information given to patients by their provider has profound implications regarding adherence and compliance with their prescribed treatment plan.

The aim of this research is to gather and analyze design ideas for a video-based remote clinical consultation application that uses AI to detect communication elements, such as nonverbal cues, during simulated consultations. The application is intended for use by health professions students and professionals in simulation-based training to develop nonverbal communication and video consultation skills. It provides feedback based on predefined rules applied to communication detected by AI. We conducted semi-structured interviews with 11 health care professionals and educators to explore their perspectives on video consultations, telehealth training in health professions education, and the methods used to teach nonverbal communication in these programs. The interview data were analyzed using inductive thematic analysis. The findings present key themes that can be used to inform design decisions for simulated video consultation situations, automated recognition of nonverbal communication in video consultation simulation, and feedback derived from it. We found that more objective measures of communication skills could enhance health professions education simulations. In addition, automatic feedback should focus on highlighting positive aspects of communication, limit low-level detail, and provide contextual information to support reflection. Finally, incorporating emotionally charged scenarios is valuable for teaching nonverbal communication skills.

Although the significance of big data and data science in predicting health outcomes and identifying causal factors is widely recognized, their application in health disparities research remains limited. Understanding health disparities in the visually impaired population requires examining their health behavior patterns and health literacy levels, which can longitudinally impact their health and well-being. In prior research, one of the authors of the study conducted an online survey with 2718 participants using 5 validated self-reported questionnaires, such as Health Promoting Lifestyle Profile II, Health Literacy Questionnaire, eHealth Literacy Questionnaire, General Self-Efficacy, and Center for Epidemiological Studies Depression. Analysis of the online survey data demonstrated that individuals with blindness exhibited significantly higher levels of health-promoting behaviors, health literacy, and eHealth literacy compared to those with moderate and severe low vision. In this study, the research team developed an R Shiny web application as a follow-up to the online survey to disseminate its findings reproducibly and interactively. The R Shiny web application is expected to facilitate reproducible as well as interactive data analysis and sharing more efficiently than traditional methods, such as appendices or Supplementary Materials, Supplemental Digital Content 2, http://links.lww.com/CIN/A463 in academic journals. Extending the research cycle with open datasets and reproducible data analysis can deepen our understanding of health disparities and foster greater collaboration among researchers with similar interests.