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Med Teach. 2017 Sep 22:1-7. doi: 10.1080/0142159X.2017.1372565. [Epub ahead of print]

A collaborative comparison of objective structured clinical examination (OSCE) standard setting methods at Australian medical schools.

Author information

1
a College of Medicine and Dentistry , James Cook University , Townsville , Australia.
2
b School of Medicine , Deakin University , Geelong , Australia.
3
c School of Medicine , University of Tasmania , Hobart , Australia.
4
d School of Medicine , University of Wollongong , Wollongong , Australia.
5
e School of Health Professions Education , Maastricht University , Maastricht , The Netherlands.

Abstract

BACKGROUND:

A key issue underpinning the usefulness of the OSCE assessment to medical education is standard setting, but the majority of standard-setting methods remain challenging for performance assessment because they produce varying passing marks. Several studies have compared standard-setting methods; however, most of these studies are limited by their experimental scope, or use data on examinee performance at a single OSCE station or from a single medical school. This collaborative study between 10 Australian medical schools investigated the effect of standard-setting methods on OSCE cut scores and failure rates.

METHODS:

This research used 5256 examinee scores from seven shared OSCE stations to calculate cut scores and failure rates using two different compromise standard-setting methods, namely the Borderline Regression and Cohen's methods.

RESULTS:

The results of this study indicate that Cohen's method yields similar outcomes to the Borderline Regression method, particularly for large examinee cohort sizes. However, with lower examinee numbers on a station, the Borderline Regression method resulted in higher cut scores and larger difference margins in the failure rates.

CONCLUSION:

Cohen's method yields similar outcomes as the Borderline Regression method and its application for benchmarking purposes and in resource-limited settings is justifiable, particularly with large examinee numbers.

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