Evaluating the Effectiveness of Expert Modeling Videos on Nursing Student Competency Behaviors.

BACKGROUND: Expert modeling videos (EMVs) have shown promise in improving students' performance in simulation. However, research evaluating the impact of EMVs is limited to major performance areas, lacking exploration into specific student competency behaviors. PURPOSE: This study evaluated the effect of an EMV intervention on undergraduate nursing students' behavioral competencies as measured by the Creighton Competency Evaluation Instrument (CCEI). METHODS: Using a quasi-experimental pragmatic evaluation design, students in a medical surgical nursing course (n = 160) viewed either an expert model demonstration video (experimental) or expert model discussion video (control). Students' behavioral competencies were measured and compared between groups using the CCEI. RESULTS: Students who viewed an expert model demonstration video performed at a higher level of competency in 11 of the 18 CCEI behaviors. CONCLUSION: Using EMVs in nursing simulation may improve students' ability to achieve clinical competency in nursing specific behaviors.

Generative AI Backstories for Simulation Preparation.

BACKGROUND: Developing engaging presimulation learning materials that provide contextualized patient information is needed to best prepare students for nursing simulation. One emerging strategy that can be used by educators to create visual images for storytelling is generative artificial intelligence (AI). PURPOSE: The purpose of this pilot study was to determine how the use of generative AI-created patient backstories as a presimulation strategy might affect student engagement and learning in nursing simulation. METHODS: A qualitative cross-sectional survey with content analysis was completed with undergraduate nursing students following an acute care simulation. RESULTS: Student surveys point to positive pedagogical outcomes of using AI image generation as a strategy to prepare for simulation such as decreased anxiety in simulation, increased preparatory knowledge, and increased emotional connection with the patient's story. CONCLUSIONS: Images created with generative AI hold promise for future research and transforming nursing education.

Effects of Expert Modeling Videos on the Development of Nursing Students' Clinical Competence.

BACKGROUND: Nurse educators are called to develop innovative teaching strategies to build clinical competency. Expert modeling videos (EMVs) promote clinical competency by demonstrating exemplar nursing care. METHODS: A quasiexperimental research study was conducted using a prebriefing intervention of a nursing process video for demonstration (experimental) and discussion (control) groups of nursing students. Simulation competency was measured using the Creighton Competency Evaluation Instrument (CCEI). RESULTS: Students in the experimental group demonstrated a statistically significant improvement in simulation competency (p = .001) and performed significantly better in the CCEI domains of communication (p = .009) and patient safety (p = .002). CONCLUSION: The use of EMVs in simulation prebriefing is an innovative teaching strategy to prepare undergraduate nursing students with the knowledge and skills required to enter the simulated environment. Through watching an exemplar demonstration of desired behaviors, students significantly improved their simulation performance and demonstrated clinically competent care of an acutely ill simulated patient. [J Nurs Educ. 2023;62(8):454-460.].

Use of Expert Modeling Videos in Undergraduate Nursing Education: A Systematic Review.

BACKGROUND: Novice graduate nurses are failing to demonstrate competent patient care in today's complex health care environment. Therefore, nurse educators have begun to critically explore educational methods, through the use of technology, that may affect student learning and clinical competency. Expert modeling videos is one strategy that allows students to observe expert behavior prior to practice and build exemplary care. METHOD: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for conducting a systematic review. Inclusion criteria for articles were: (1) experimental and nonexperimental, (2) peer reviewed, (3) primary research, (4), published in English, and (5) written through January 2021. RESULTS: Fifteen studies were identified that met all of the inclusion criteria. CONCLUSION: Expert modeling videos have become an attractive educational strategy to promote learning in undergraduate nursing students. Future studies should focus on the use of these videos as a pedagogical strategy to transform learning in nursing education. [J Nurs Educ. 2023;62(2):89-96.].