Julia Walker, professor, Angela Richard-Eaglin, assistant professor, Akhil Hegde, research scientist, and Virginia Muckler, associate professor, wrote "A deep learning approach to student registered nurse anesthetist (SRNA) education" in the International Journal of Nursing Education Scholarship. 

Abstract

Objectives: This manuscript describes the application of deep learning to physiology education of Student Registered Nurse Anesthetists (SRNA) and the benefits thereof. A strong foundation in physiology and the ability to apply this knowledge to challenging clinical situations is crucial to the successful SRNA. Deep learning, a well-studied pedagogical technique, facilitates development and long-term retention of a mental knowledge framework that can be applied to complex problems. Deep learning requires the educator to facilitate the development of critical thinking and students to actively learn and take responsibility for gaining knowledge and skills.

Methods: We applied the deep learning approach, including flipped classroom and problem-based learning, and surveyed SRNA students (n=127) about their learning experience.

Results: Survey responses showed that the majority of students favored the deep learning approach and thought it advanced their critical thinking skills.

Conclusions: SRNAs reported that their physiology knowledge base and critical thinking benefited from the use of the deep learning strategy.

Citation

Walker, J., Richard-Eaglin, A., Hegde, A., & Muckler, V. C. (2021). A deep learning approach to student registered nurse anesthetist (SRNA) education. International journal of nursing education scholarship, 18(1), 10.1515/ijnes-2020-0068. https://doi.org/10.1515/ijnes-2020-0068