• G Jaconia, C Naus, and A Lee.
• Columbia University Irving Medical Center, New York, NY, USA.
• Int J Obstet Anesth. 2023 Nov 1; 56: 103923103923.

BackgroundLearning to perform neuraxial techniques in obstetrics is considered one of the most difficult skills for anesthesiology trainees to acquire and no consensus exists regarding the best practices for teaching these procedures. Utilizing a qualitative, phenomenological approach, we aimed to explore what trainees perceive as the best approaches to teaching epidural anesthesia techniques; identify how these perceptions align or differ from those of faculty anesthesiologists; and examine how these approaches fit into the cognitive apprenticeship framework, which describes a process of reflection on how learning occurs in the authentic environment.MethodsSemi-scripted interviews were conducted with 10 residents and three faculty members from the division of obstetric anesthesiology at an academic center. Interviews were transcribed, de-identified, fragmented, and coded. A thematic analysis was conducted, and codes re-organized into the cognitive apprenticeship framework of (1) content, (2) method (including subcategories modeling, coaching, scaffolding, articulation, reflection, and exploration), (3) sequence, and (4) sociology (including situated learning and culture of expert practice).ResultsTrainees valued a staged approach to learning epidural techniques, independent trouble-shooting, graded independence, focused feedback, and a calm instructor. The challenges of learning and teaching epidural techniques identified by trainees and instructors included the tactile nature of the procedure, teaching on awake, non-sedated patients, limited teaching time, and creating an environment of psychological safety.ConclusionTrainee and instructor preferences for teaching epidural procedures in obstetrics aligned with the cognitive apprenticeship framework. These concepts may be applied to curriculum design, evaluation, feedback, self-assessment and faculty development.Copyright © 2023 Elsevier Ltd. All rights reserved.

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