Racial and ethnic minorities disproportionately suffer the burden of adverse health outcomes in the United States. Increasing the diversity of healthcare providers may help decrease disparities in outcomes. Unfortunately, language barriers may affect performance in nursing school and credentialing examinations. The purpose of this exploratory study was to identify current practices and trends affecting the translation of credentialing examinations. Commissioned by the National Board of Certification and Recertification for Nurse Anesthetists, a survey was sent to the credentialing organizations soliciting information about their exam translation practices and considerations. Among the 27 credentialing organizations (two licensure and 25 certification organizations) that completed the survey, 63% were from healthcare. All the organizations offered their credentialing examinations in English. Some offered their examination in Chinese/Mandarin (15%), Spanish (11%), French (7%), and Arabic (7%). The majority (78%) do not translate their examinations into another language. Among the six credentialing organizations translating their examinations, 67% translate one, and 17% translate two examinations. Most use the forward and back-translation techniques. For organizations embarking on a multilingual credentialing program, it is imperative to ensure psychometric equivalence of their examinations. Translation can help ensure that candidates are tested on their intended competencies, not their language proficiency.
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JMIR Med Educ
December 2024
Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan.
This study evaluated the performance of ChatGPT with GPT-4 Omni (GPT-4o) on the 118th Japanese Medical Licensing Examination. The study focused on both text-only and image-based questions. The model demonstrated a high level of accuracy overall, with no significant difference in performance between text-only and image-based questions.
View Article and Find Full Text PDFAm J Orthod Dentofacial Orthop
January 2025
American Board of Orthodontics, Mesa, Ariz, and San Juan, PR, and Penfield, NY, and Chesterton, IN, and Castle Rock, Colo, and Lincoln, Nebr, and Oklahoma City, Okla, and Charleston, South Carolina, and Montgomery, Ala.
This article outlines the development process for the scenario-based clinical examination of the American Board of Orthodontics (ABO). It emphasizes the importance of gaining hands-on experience with patients and critically analyzing the facts when formulating sound clinical judgments. These exercises enhance critical thinking skills, allowing for self-assessment and reflection on patient outcomes.
View Article and Find Full Text PDFJ Grad Med Educ
December 2024
is Associate Professor of Anesthesiology, Department of Anesthesiology, University of Utah School of Medicine, and Affiliate Faculty, Global Change and Sustainability Center, University of Utah, Salt Lake City, Utah, USA.
With an increased focus on climate change in graduate medical education (GME), the environmental implications of travel for board certification examinations remain poorly described. The return to the mandatory in-person applied examination (AE) for board eligible anesthesiologists presents potentially sizeable greenhouse gas (GHG) emissions when compared to the virtual format administered during the COVID-19 pandemic. To estimate the GHG emissions from air travel to the in-person AE and discuss its implications for various specialties as they return to in-person examinations.
View Article and Find Full Text PDFPac Symp Biocomput
December 2024
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
The United States Medical Licensing Examination (USMLE) is a critical step in assessing the competence of future physicians, yet the process of creating exam questions and study materials is both time-consuming and costly. While Large Language Models (LLMs), such as OpenAI's GPT-4, have demonstrated proficiency in answering medical exam questions, their potential in generating such questions remains underexplored. This study presents QUEST-AI, a novel system that utilizes LLMs to (1) generate USMLE-style questions, (2) identify and flag incorrect questions, and (3) correct errors in the flagged questions.
View Article and Find Full Text PDFAim: The objective of the present study was to investigate the clinical understanding and reasoning abilities of large language models (LLMs); namely, ChatGPT, GPT-4, and New Bing, by evaluating their performance in the NDLE (National Dental Licensing Examination) in China.
Materials And Methods: Questions from the NDLE from 2020 to 2022 were selected based on subject weightings. Standardized prompts were utilized to regulate the output of LLMs for acquiring more precise answers.
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