An ever-changing healthcare landscape requires today’s nurses to have a solid foundation in knowledge and clinical judgment to provide safe care to patients. Nurse educators must implement teaching strategies that help develop the knowledge and clinical judgment that nursing students will need upon graduation and entry into healthcare. Simulation-based experiences have been shown to help develop clinical judgment when used as part of a clinical practicum. However, few studies have examined the effectiveness of simulation-based experiences as a classroom teaching strategy. A quasi-experimental study was conducted to examine knowledge acquisition, clinical judgment, and general self-efficacy in undergraduate nursing students who participated in simulation-based case studies as a classroom teaching strategy versus those students who attended a traditional lecture.
Students in the intervention group rotated through four simulation-based case study stations. Results indicated that there was not a significant difference in knowledge, clinical judgment, or general self-efficacy found between nursing students participating in simulation-based case studies versus those attending a traditional lecture. Additionally, relationships between demographic characteristics and clinical judgment scores in undergraduate nursing students were explored. There were no statistically significant relationships found between demographic characteristics and clinical judgment in this sample. Further analysis indicated that both teaching strategies are effective in promoting knowledge acquisition, clinical judgment, and general self-efficacy. The findings of this study demonstrate that both participation in simulation-based case studies and attending a traditional lecture are effective classroom teaching strategies in promoting knowledge acquisition, clinical judgment, and general self-efficacy in nursing students. Nurse educators are encouraged to continue to explore simulation-based experiences as a teaching strategy in the classroom.
Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/rvv0-mg22 |
Date | January 2022 |
Creators | Becnel, Kesha Trosclair |
Source Sets | Columbia University |
Language | English |
Detected Language | English |
Type | Theses |
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