Nurse Educator最新文献

Background: Nursing interns face early clinical distress; chatbot-based mental health tools show promise, although evidence of their feasibility and educational value remains insufficient.

Purpose: To evaluate the application feasibility and effectiveness of the Xiao Ling Assistant Chatbot (X-LAC) for addressing mental health challenges, monitoring stress, and detecting early warning signals of psychological distress among nursing students participating in a clinical internship.

Methods: A 4-week single-group study with 61 nursing interns tested X-LAC's daily chatbot-based support and keyword-triggered alerts; pre/post mental health and stress were assessed.

Results: Well-being improved (12-20); suicidal ideation declined (10-4). The chatbot flagged 16 high-risk expressions per 100 messages, notably so tired and under pressure; 60.6% reported internship distress.

Conclusion: Chatbot support shows promise for clinical training, reducing stress and enabling early detection. Integrating artificial intelligence for risk prediction is warranted while retaining human oversight for critical cases.

Background: Nurse practitioners can help fill needs for primary care providers but first must successfully transition from registered nurse through graduate education, which presents challenges and opportunities for growth.

Purpose: The purpose of this study was to describe experiences of nurses during the first semester of graduate school.

Methods: Reflections of 23 students were analyzed by nursing faculty using qualitative descriptive methods.

Results: Five main concepts were identified by students: Academic performance, Challenges, Emotions, Linking coursework to future role, and Support.

Conclusion: Reflective journaling may help students process and adapt to challenges of graduate school.

Background: Integration of knowledge graphs (KGs) and large language models (LLMs) holds transformative potential for nursing education, particularly in research methodology and statistical literacy. This proof-of-concept study developed a Nursing Research Education Agent to support learners in understanding research designs and statistical concepts.

Purpose: To evaluate the agent's feasibility, pedagogical alignment, and AI performance using validated instruments in a nursing education context.

Methods: The agent combined structured KGs of nursing research knowledge with LLMs for interactive, natural-language responses. Ten nursing educators assessed it using the 10-item Pedagogical Fit Evaluation Scale and 10-item AI Performance Evaluation Scale.

Results: Educators rated pedagogical fit highly (M = 4.20, SD = 0.63) and AI performance strongly (M = 4.10, SD = 0.56), praising clinical relevance, accuracy, and promotion of critical thinking. Integration into curricula was deemed feasible.

Conclusions: KG-LLM-integrated agents show strong promise for nursing research education. Further development and larger-scale trials are recommended.

Background: With AI technologies increasingly integrated into nursing education, enhancing AI literacy among nursing students is critical, yet influencing factors remain understudied.

Purpose: This study assessed AI literacy levels among nursing students, identified key influencing factors, and examined the mediating role of professional commitment between campus environmental support and AI literacy.

Methods: A cross-sectional survey was conducted with 1680 nursing students from 6 medical colleges in Anhui Province, China, using convenience sampling. Data were collected through structured scales and analyzed via multiple linear regression and mediation models.

Results: AI literacy was moderately high, positively influenced by urban residence and prior AI training. Campus support directly enhanced it (β = 0.675), with professional commitment serving as a partial mediator (indirect β = 1.857).

Conclusions: To enhance AI literacy, increasing campus AI resources, improving educational environments, and strengthening professional commitment are recommended, integrating AI competency into nursing education.

Background: Enrollment as a student in a health professions program is reported to be stressful and to impact psychosocial health (PH).

Purpose: A concept analysis of PH was conducted using Rodgers' method in the context of these programs.

Methods: Independent, blinded PRISMA review of 3719 works revealed 35 relevant studies. Narrative summary tables were derived based on evidence synthesis. Antecedents, attributes, consequences, a model case, and a conceptual model were identified.

Results: Pre-conditioned by students' developmental, social, and environmental characteristics, attributes of PH were: (1) mental health, stress, coping, locus of control; (2) resilience, emotional intelligence, mindfulness, hope; (3) self-esteem; (4) social support; (5) physical health, self-care; (6) academic work-life balance; and (7) academic pressure, workload, external influences.

Conclusions: PH has tangible impacts on academic and clinical performance. Efforts by educators and practicing clinicians to enhance students' PH make a difference.

Background: The next generation of PhD-prepared nurses must be forward-thinking and equipped to solve existing and emerging health care problems requiring educators to reimagine curricula to surpass conventional scientific methods.

Problem: Limited guidance exists on effectively integrating human-centered design (HCD) with scientific methods in doctoral education.

Approach: Grounded in a learner-centered paradigm, faculty developed a doctoral-level innovation and intervention course that blended HCD with traditional research methodologies. This article explores the course's philosophical approach, design and implementation, highlighting challenges as well as areas of convergence and divergence between the 2 approaches. Emphasis is placed on how these dynamics shaped teaching strategies and student learning.

Outcome: A pedagogical model emerged structured around the 3 integrated domains of the course: design thinking, philosophical and theoretical frameworks, and complementary research methods.

Conclusions: The model supports the development of adaptive nurse scientists equipped to engage with complex health care problems through iterative, stakeholder-informed inquiry.