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Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure

BACKGROUND: Struggling medical students is an under-researched in medical education. It is known, however, that early identification is important for effective remediation. The aim of the study was to determine the predictive effect of medical school admission tools regarding whether a student will...

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Autores principales: Li, James, Thompson, Rachel, Shulruf, Boaz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842496/
https://www.ncbi.nlm.nih.gov/pubmed/31706306
http://dx.doi.org/10.1186/s12909-019-1860-z
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author Li, James
Thompson, Rachel
Shulruf, Boaz
author_facet Li, James
Thompson, Rachel
Shulruf, Boaz
author_sort Li, James
collection PubMed
description BACKGROUND: Struggling medical students is an under-researched in medical education. It is known, however, that early identification is important for effective remediation. The aim of the study was to determine the predictive effect of medical school admission tools regarding whether a student will struggle academically. METHODS: Data comprise 700 students from the University of New South Wales undergraduate medical program. The main outcome of interest was whether these students struggled during this 6-year program; they were classified to be struggling they failed any end-of-phase examination but still graduated from the program. Discriminate Function Analysis (DFA) assessed whether their pre-admission academic achievement, Undergraduate Medicine Admission Test (UMAT) and interview scores had predictive effect regarding likelihood to struggle. RESULTS: A lower pre-admission academic achievement in the form of Australian Tertiary Admission Rank (ATAR) or Grade Point Average (GPA) were found to be the best positive predictors of whether a student was likely to struggle. Lower UMAT and poorer interview scores were found to have a comparatively much smaller predictive effect. CONCLUSION: Although medical admission tests are widely used, medical school rarely use these data for educational purposes. The results of this study suggest admission test data can predict who among the admitted students is likely to struggle in the program. Educationally, this information is invaluable. These results indicate that pre-admission academic achievement can be used to predict which students are likely to struggle in an Australian undergraduate medicine program. Further research into predicting other types of struggling students as well as remediation methods are necessary.
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spelling pubmed-68424962019-11-14 Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure Li, James Thompson, Rachel Shulruf, Boaz BMC Med Educ Research Article BACKGROUND: Struggling medical students is an under-researched in medical education. It is known, however, that early identification is important for effective remediation. The aim of the study was to determine the predictive effect of medical school admission tools regarding whether a student will struggle academically. METHODS: Data comprise 700 students from the University of New South Wales undergraduate medical program. The main outcome of interest was whether these students struggled during this 6-year program; they were classified to be struggling they failed any end-of-phase examination but still graduated from the program. Discriminate Function Analysis (DFA) assessed whether their pre-admission academic achievement, Undergraduate Medicine Admission Test (UMAT) and interview scores had predictive effect regarding likelihood to struggle. RESULTS: A lower pre-admission academic achievement in the form of Australian Tertiary Admission Rank (ATAR) or Grade Point Average (GPA) were found to be the best positive predictors of whether a student was likely to struggle. Lower UMAT and poorer interview scores were found to have a comparatively much smaller predictive effect. CONCLUSION: Although medical admission tests are widely used, medical school rarely use these data for educational purposes. The results of this study suggest admission test data can predict who among the admitted students is likely to struggle in the program. Educationally, this information is invaluable. These results indicate that pre-admission academic achievement can be used to predict which students are likely to struggle in an Australian undergraduate medicine program. Further research into predicting other types of struggling students as well as remediation methods are necessary. BioMed Central 2019-11-09 /pmc/articles/PMC6842496/ /pubmed/31706306 http://dx.doi.org/10.1186/s12909-019-1860-z Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Li, James
Thompson, Rachel
Shulruf, Boaz
Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure
title Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure
title_full Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure
title_fullStr Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure
title_full_unstemmed Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure
title_short Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure
title_sort struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842496/
https://www.ncbi.nlm.nih.gov/pubmed/31706306
http://dx.doi.org/10.1186/s12909-019-1860-z
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