Use of Sociodemographic Information in Clinical Vignettes of Multiple-Choice Questions for Preclinical Medical Students.

Demographics Multiple-choice Vignette

Journal

Medical science educator
ISSN: 2156-8650
Titre abrégé: Med Sci Educ
Pays: United States
ID NLM: 101625548

Informations de publication

Date de publication:
Jun 2023
Historique:
accepted: 21 03 2023
pmc-release: 01 06 2024
medline: 28 7 2023
pubmed: 28 7 2023
entrez: 28 7 2023
Statut: epublish

Résumé

This paper aims to characterize the use of demographic data in multiple-choice questions from a commercial preclinical question bank and determine if there is appropriate use of different distractors. Multiple-choice questions for medical students often include vignettes describing a patient's presentation to help guide students to a diagnosis, but overall patterns of usage between different types of nonmedical patient information in question stems have yet to be determined. Three hundred eighty of 453 randomly selected questions were included for analysis after determining they contained a clinical vignette and required a diagnosis. The vignettes and following explanations were then examined for the presence/absence of 11 types of demographic information, including age, sex/gender, and socioeconomic status. We compared both the usage frequency and relevance between the 11 information types. Most information types were present in less than 10% of clinical vignettes, but age and sex/gender were present in over 95% of question stems. Over 50% of questions included irrelevant information about age and sex/gender, but 75% of questions did not include any irrelevant information of other types. Patient weight and environmental exposures were significantly more likely to be relevant than age or sex/gender. Students using the questions in this study will frequently gain practice incorporating age and sex/gender into their clinical reasoning while receiving little exposure to other demographic information. Based on our findings, we posit that questions could include more irrelevant information, outside age and sex/gender, to better approximate real clinical scenarios and ensure students do not overvalue certain demographic data. The online version contains supplementary material available at 10.1007/s40670-023-01778-z.

Sections du résumé

Purpose UNASSIGNED
This paper aims to characterize the use of demographic data in multiple-choice questions from a commercial preclinical question bank and determine if there is appropriate use of different distractors.
Background UNASSIGNED
Multiple-choice questions for medical students often include vignettes describing a patient's presentation to help guide students to a diagnosis, but overall patterns of usage between different types of nonmedical patient information in question stems have yet to be determined.
Methods UNASSIGNED
Three hundred eighty of 453 randomly selected questions were included for analysis after determining they contained a clinical vignette and required a diagnosis. The vignettes and following explanations were then examined for the presence/absence of 11 types of demographic information, including age, sex/gender, and socioeconomic status. We compared both the usage frequency and relevance between the 11 information types.
Results UNASSIGNED
Most information types were present in less than 10% of clinical vignettes, but age and sex/gender were present in over 95% of question stems. Over 50% of questions included irrelevant information about age and sex/gender, but 75% of questions did not include any irrelevant information of other types. Patient weight and environmental exposures were significantly more likely to be relevant than age or sex/gender.
Discussion UNASSIGNED
Students using the questions in this study will frequently gain practice incorporating age and sex/gender into their clinical reasoning while receiving little exposure to other demographic information. Based on our findings, we posit that questions could include more irrelevant information, outside age and sex/gender, to better approximate real clinical scenarios and ensure students do not overvalue certain demographic data.
Supplementary Information UNASSIGNED
The online version contains supplementary material available at 10.1007/s40670-023-01778-z.

Identifiants

pubmed: 37501800
doi: 10.1007/s40670-023-01778-z
pii: 1778
pmc: PMC10368604
doi:

Types de publication

Journal Article

Langues

eng

Pagination

659-667

Informations de copyright

© The Author(s) under exclusive licence to International Association of Medical Science Educators 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Déclaration de conflit d'intérêts

Conflict of InterestThe authors declare no competing interests.

Références

Teach Learn Med. 2017 Apr-Jun;29(2):115-122
pubmed: 28051889
Soc Sci Med. 2014 Feb;103:126-133
pubmed: 24507917
J Health Care Poor Underserved. 1998 May;9(2):117-25
pubmed: 10073197
Acad Med. 2020 Dec;95(12):1817-1822
pubmed: 32590465
Genome Res. 2004 Sep;14(9):1679-85
pubmed: 15342553
CMAJ. 2016 Dec 6;188(17-18):E474-E483
pubmed: 27503870
Acad Med. 2016 Jul;91(7):916-20
pubmed: 27166865
Teach Learn Med. 2022 Jun 6;:1-9
pubmed: 35668558
Med Educ. 2021 May;55(5):595-603
pubmed: 33354809
J Educ Eval Health Prof. 2018;15:28
pubmed: 30541188
Science. 2016 Feb 5;351(6273):564-5
pubmed: 26912690
Acad Med. 2010 Apr;85(4):702-5
pubmed: 20354391
Proc Natl Acad Sci U S A. 2016 Apr 19;113(16):4296-301
pubmed: 27044069

Auteurs

Kelly Carey-Ewend (K)

Gillings School of Global Public Health, Chapel Hill, NC USA.

Amir Feinberg (A)

University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC USA.

Alexis Flen (A)

University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC USA.

Clark Williamson (C)

University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC USA.

Carmen Gutierrez (C)

University of North Carolina at Chapel Hill, Chapel Hill, NC USA.

Samuel Cykert (S)

Division of General Medicine and Clinical Epidemiology, University of North Carolina School of Medicine, Chapel Hill, NC USA.

Gary L Beck Dallaghan (GL)

University of Texas at Tyler School of Medicine, Tyler, TX USA.

Kurt O Gilliland (KO)

University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC USA.

Classifications MeSH