Showing 1 - 5 of 5 Research Library Publications
Posted: | Victoria Yaneva, Janet Mee, Le Ha, Polina Harik, Michael Jodoin, Alex Mechaber

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - p 2880–2886

 

This paper presents a corpus of 43,985 clinical patient notes (PNs) written by 35,156 examinees during the high-stakes USMLE® Step 2 Clinical Skills examination.

Posted: | Y.S. Park, A. Morales, L. Ross, M. Paniagua

Evaluation & the Health Professions: Volume: 43 issue: 3, page(s): 149-158

 

This study examines the innovative and practical application of DCM framework to health professions educational assessments using retrospective large-scale assessment data from the basic and clinical sciences: National Board of Medical Examiners Subject Examinations in pathology (n = 2,006) and medicine (n = 2,351).

Posted: | J. Salt, P. Harik, M. A. Barone

Academic Medicine: July 2019 - Volume 94 - Issue 7 - p 926-927

 

A response to concerns regarding potential bias in the implementation of machine learning (ML) to scoring of the United States Medical Licensing Examination Step 2 Clinical Skills (CS) patient notes (PN).

Posted: | M. von Davier, J. H. Shin, L. Khorramdel, L. Stankov

Applied Psychological Measurement: Volume: 42 issue: 4, page(s): 291-306

 

The research presented in this article combines mathematical derivations and empirical results to investigate effects of the nonparametric anchoring vignette approach proposed by King, Murray, Salomon, and Tandon on the reliability and validity of rating data. The anchoring vignette approach aims to correct rating data for response styles to improve comparability across individuals and groups.

Posted: | Z. Jiang, M.R. Raymond

Applied Psychological Measurement: Volume: 42 issue: 8, page(s): 595-612

 

Conventional methods for evaluating the utility of subscores rely on reliability and correlation coefficients. However, correlations can overlook a notable source of variability: variation in subtest means/difficulties. Brennan introduced a reliability index for score profiles based on multivariate generalizability theory, designated as G, which is sensitive to variation in subtest difficulty. However, there has been little, if any, research evaluating the properties of this index. A series of simulation experiments, as well as analyses of real data, were conducted to investigate G under various conditions of subtest reliability, subtest correlations, and variability in subtest means.