Medical Science Educator最新文献

We assessed the performance of OpenAI's ChatGPT-4 on United States Medical Licensing Exam STEP 1 style questions across the systems and disciplines appearing on the examination. ChatGPT-4 answered 86% of the 1300 questions accurately, exceeding the estimated passing score of 60% with no significant differences in performance across clinical domains. Findings demonstrated an improvement over earlier models as well as consistent performance in topics ranging from complex biological processes to ethical considerations in patient care. Its proficiency provides support for the use of artificial intelligence (AI) as an interactive learning tool and furthermore raises questions about how the technology can be used to educate students in the preclinical component of their medical education. The authors provide an example and discuss how students can leverage AI to receive real-time analogies and explanations tailored to their desired level of education. An appropriate application of this technology potentially enables enhancement of learning outcomes for medical students in the preclinical component of their education.

We describe our institution's development and implementation of our Capstone course from a small elective course to the only required fourth-year course. The course's structure evolved from mostly didactic to one including various workshops and simulation sessions. Course content has become increasingly specialty-specific. Implementation requires high faculty and resident involvement. Evaluations indicate a positive impact of the course on participants' self-reported confidence and residency preparedness. Assessment remains pass/fail with more specialty-specific questions. As steadily increasing numbers of medical schools are developing transition to residency courses, we share our Capstone course's evolution and lessons learned over the past nine years.

Supplementary information: The online version contains supplementary material available at 10.1007/s40670-023-01880-2.

Learning communities (LCs) are a type of high-impact, collaborative educational practice where students cooperate with other students in two or more classes to improve learning. Additional criteria used for defining LCs vary across the literature and have focused on high school and undergraduate programs. Similar benefits may also be experienced in professional programs; however, before further studies can investigate and describe these benefits, the structure for LCs in the veterinary curriculum must first be defined. To accomplish this, a survey instrument was developed and distributed to veterinary students attending Iowa State University College of Veterinary Medicine during the 2019 to 2020 academic year. The survey focused on a number of criteria and included both qualitative and quantitative questions. Criteria included demographics, prior experiences, and structure of an ideal LC. Recurring themes in the qualitative responses were identified, reviewed, and categorized based on an established coding theme. Significant correlations included an association between respondents' opinions on the ideal number of people in a LC and prior participation in LCs; age and preference for LC focus; ideal number of LC members and LC focus; and gender identity and thoughts on LC diversity. Based on the survey results, the typical veterinary student would consider an ideal LC to (1) be selected by the students of the LC, (2) have faculty mentors, (3) have a focus on coursework with some social aspects, (4) have fewer than seven members, (5) be composed of friends, (6) meet both during and after class, and (7) be diverse.