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Related Experiment Video

Updated: May 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Improving student selection using multiple mini-interviews with multifaceted Rasch modeling.

Hettie Till1, Carol Myford, Jonathan Dowell

  • 1Medical School, University of Dundee, Scotland, United Kingdom. hettietill@gmail.com

Academic Medicine : Journal of the Association of American Medical Colleges
|December 28, 2012
PubMed
Summary
This summary is machine-generated.

The multiple mini-interview (MMI) effectively differentiates medical school candidates. However, examiner differences impact scores, necessitating fair average scoring and quality control for equitable selection.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Last Updated: May 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Medical education research
  • Psychometrics
  • Admissions processes

Background:

  • The multiple mini-interview (MMI) is a widely used admissions tool.
  • Assessing noncognitive attributes is crucial for medical school selection.
  • Examiner variability can introduce bias in assessment.

Purpose of the Study:

  • To evaluate the effectiveness of the MMI in differentiating medical school candidates.
  • To investigate systematic differences in rating patterns among examiner groups (staff, students, simulated patients).
  • To determine the impact of examiner differences on candidate scores.

Main Methods:

  • Analysis of data from 452 candidates assessed using a 10-station MMI measuring six noncognitive attributes.
  • Application of Rasch modeling (Facets software) to analyze candidate, examiner, and station effects.
  • Calculation of fair average scores adjusted for examiner severity/leniency and station difficulty.

Main Results:

  • The MMI demonstrated high reliability (0.89) in separating candidates into four distinct noncognitive ability levels.
  • Rasch measures explained 31.69% of rating variance, with candidates (16.01%) and examiners (11.32%) being significant factors.
  • Student examiners rated more severely and exhibited more variability than staff examiners.
  • Adjusting scores for examiner and station effects would have altered selection outcomes for 9.6% of candidates.

Conclusions:

  • Quality control monitoring of examiners and stations is essential for MMI fairness.
  • Identifying and addressing examiner variability (e.g., through training) is crucial.
  • Utilizing fair average scores is recommended for equitable candidate ranking in MMI processes.