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RESEARCH LIBRARY

View the latest publications from members of the NBME research team

Showing 31 - 40 of 113 Research Library Publications
Posted: | Victoria Yaneva, Brian E. Clauser, Amy Morales, Miguel Paniagua

Advances in Health Sciences Education: Volume 27, p 1401–1422

 

After collecting eye-tracking data from 26 students responding to clinical MCQs, analysis is performed by providing 119 eye-tracking features as input for a machine-learning model aiming to classify correct and incorrect responses. The predictive power of various combinations of features within the model is evaluated to understand how different feature interactions contribute to the predictions.

Posted: | Andrew A. White, Ann M. King, Angelo E. D’Addario, Karen Berg Brigham, Suzanne Dintzis, Emily E. Fay, Thomas H. Gallagher, Kathleen M. Mazor

JMIR Medical Education: Volume 8 - Issue 2 - e30988

 

This article aims to compare the reliability of two assessment groups (crowdsourced laypeople and patient advocates) in rating physician error disclosure communication skills using the Video-Based Communication Assessment app.

Posted: | Jonathan D. Rubright, Michael Jodoin, Stephanie Woodward, Michael A. Barone

Academic Medicine: Volume 97 - Issue 5 - Pages 718-722

 

The purpose of this 2019–2020 study was to statistically identify and qualitatively review USMLE Step 1 exam questions (items) using differential item functioning (DIF) methodology.

Posted: | Katie L. Arnhart, Monica M. Cuddy, David Johnson, Michael A. Barone, Aaron Young

Academic Medicine: Volume 97 - Issue 4 - Pages 476-477

 

Response to to emphasize that although findings support a relationship between multiple USMLE attempts and increased likelihood of receiving disciplinary actions, the findings in isolation are not sufficient for proposing new policy on how many attempts should be allowed.

Posted: | Katie L. Arnhart, Monica M. Cuddy, David Johnson, Michael A. Barone, Aaron Young

Academic Medicine: Volume 97 - Issue 4 - Pages 467-477

 

Letter to the editor; response to D'Eon and Kleinheksel.

Posted: | Richard A. Feinberg, Carol Morrison, Mark R. Raymond

Educational Measurement: Issues and Practices: Volume 41 - Issue 1 - Pages 95-96

 

Often unanticipated situations arise that can create a range of problems from threats to score validity, to unexpected financial costs, and even longer-term reputational damage. This module discusses some of these unusual challenges that usually occur in a credentialing program.

Posted: | Andrew A White, Ann M King, Angelo E D’Addario, Karen Berg Brigham, Suzanne Dintzis, Emily E Fay, Thomas H Gallagher, Kathleen M Mazor

JMIR Medical Education: Volume 8 , Issue 4

 

The Video-based Communication Assessment (VCA) app is a novel tool for simulating communication scenarios for practice and obtaining crowdsourced assessments and feedback on physicians’ communication skills. This article aims to evaluate the efficacy of using VCA practice and feedback as a stand-alone intervention for the development of residents’ error disclosure skills.

Posted: | Michael Barone, Keith S. Coulter, Katina Kulow, Xingbo (Bo) Li

Psychology & Marketing: Volume 39 - Issue 6 - Pages 1190-1203

 

Does seeing a price in a physically low (vs. high) location prompt consumers to believe that the featured product is less costly? This research further specifies when price location effects are likely to arise, increasing our understanding of pricing in general and this locational phenomenon in particular.

Posted: | Monica M. Cuddy, Lauren M. Foster, Paul M. Wallach, Maya M. Hammoud, David B. Swanson

Academic Medicine: Volume 97 - Issue 2 - Pages 262-270

 

This study examined shifts in U.S. medical student interactions with EHRs during their clinical education, 2012–2016, and how these interactions varied by clerkship within and across medical schools.

Posted: | Ian Micir, Kimberly Swygert, Jean D'Angelo

Journal of Applied Technology: Volume 23 - Special Issue 1 - Pages 30-40

 

The interpretations of test scores in secure, high-stakes environments are dependent on several assumptions, one of which is that examinee responses to items are independent and no enemy items are included on the same forms. This paper documents the development and implementation of a C#-based application that uses Natural Language Processing (NLP) and Machine Learning (ML) techniques to produce prioritized predictions of item enemy statuses within a large item bank.