Background: To the best of the authors' knowledge, there are no reports describing what learners believe are good emergency medicine (EM) teaching practices. EM faculty developers are compromised by this lack of knowledge about what EM learners appreciate in their teachers.
Objectives: To determine what Canadian EM learners consider to be good prerequisites and strategies for effective teaching in the emergency department (ED).
Methods: Clinical clerks and residents from the Canadian College of Family Physicians, Emergency Medicine certification [CCFP(EM)] fellowship program, the Royal College of Physicians and Surgeons of Canada, Emergency Medicine certification [FRCP(EM)] fellowship program, and off-service programs from all five Ontario medical schools participated in monitored focus-group sessions. Conversations were recorded, transcribed by a third party, and coded by two independent assessors using standard grounded theory methods. The text was categorized based on the final code into basic themes and specific qualifiers, which were then sorted by frequency of mention in the focus groups. Results are presented in descriptive fashion.
Results: Twenty-eight learners participated. They identified 14 major principles for good EM teaching, and a further 30 specific qualifiers. The top five principles were: "has a positive teacher attitude," "takes time to teach," "uses teachable moments well," "tailors teaching to the learner," and "gives appropriate feedback." Agreement on classification of ideas was 86%.
Conclusions: Learners are sensitive to the constraints of the ED teaching environment, and have consistent views about good ED teaching practices. Among 14 general principles identified, "takes time to teach," "gives feedback," "tailors teaching to the learner," "uses teachable moments," and "has a good teacher attitude" were the most commonly reported.
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http://dx.doi.org/10.1197/j.aem.2005.04.022 | DOI Listing |
Int J Surg
January 2025
Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.
Background: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.
View Article and Find Full Text PDFJAMA
January 2025
Division of Hematology, Brigham and Women's Hospital, Boston, Massachusetts.
JAMA Intern Med
January 2025
Harvard Medical School, Boston, Massachusetts.
JAMA Intern Med
January 2025
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Importance: There are no validated decision rules for terminating resuscitation during in-hospital cardiac arrest. Decision rules may guide termination and prevent inappropriate early termination of resuscitation.
Objective: To develop and validate termination of resuscitation rules for in-hospital cardiac arrest.
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