scholarly journals A randomised controlled trial of the influence of Non-native English accents on Examiners’ scores in OSCEs

2020 ◽  
Author(s):  
An Kozato ◽  
Nimesh Patel ◽  
Kiyoshi Shikino

Abstract BackgroundObjective Structured Clinical Examinations (OSCEs) are important aspects of assessment in medical education. There are anecdotes that students with non-native English accents (NNEA) may be marked negatively due to unconscious bias. It is imperative to minimise the examiners’ bias so that the difference in the scores reflects students’ clinical competence. Research in shows NNEAs can cause stereotyping, often leading to the speaker being negatively judged. However, no medical education study has looked at the influence of NNEAs in assessment.MethodsThis is a randomised, single - blinded controlled trial. Four videos of one mock OSCE station were produced. A professional actor played a medical student. Two near identical scripts were prepared. Two videos showed the actor speaking with an Indian accent and two videos showed the actor speaking without the accent in either script. Forty-two UK OSCE examiners were recruited and randomly assigned to two groups. They watched two videos online, each with either script, with and without the NNEA. Checklist item scores were analysed with descriptive statistics and simple linear regression model. Global scores were analysed with descriptive statistics and logistic ordinal regression model.ResultsThirty-two examiners completed the study. The average scores for the checklist items (41.6 points) did not change when the accent variable was changed. Simple linear regression model showed no statistically significant relationship between the accent and the scores (Regression coefficient = 0.032, p = 0.982). For the global scores received by the videos with the NNEA, there were one less ‘Good’ grade and one more ‘Fail’ grade compared to the ones without the NNEA. Logistical ordinal regression model on global scores showed, examiners were more likely to mark the student more negatively (p < 0.0001) but also more positively (p < 0.0001) when the NNEA was present.ConclusionsExaminers could be biased either positively or negatively towards NNEAs when giving global scores. Further research is required to consider the nature of this bias. More discussion is warranted to consider how the accent should be considered in current medical education assessment.

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2423 ◽  
Author(s):  
Jiun-Jian Liaw ◽  
Yung-Fa Huang ◽  
Cheng-Hsiung Hsieh ◽  
Dung-Ching Lin ◽  
Chin-Hsiang Luo

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.


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