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Related Experiment Video

Updated: Feb 16, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.

Teresa M Chan, Jonathan Sherbino, Mathew Mercuri

    Journal of Graduate Medical Education
    |December 23, 2017
    PubMed
    Summary

    Medical resident assessments show varied starting points and progression rates. Understanding these nuances is key for accurate competency evaluation in medical education.

    Area of Science:

    • Medical Education
    • Competency-Based Assessment
    • Workplace-Based Assessment

    Background:

    • Competency-based medical education (CBME) necessitates frequent assessments to personalize trainee learning.
    • The McMaster Modular Assessment Program (MMAP) was implemented in 2012 for shift-based resident performance evaluations.

    Purpose of the Study:

    • To describe patterns, including trends and sources of variance, within aggregated workplace-based assessment data.
    • To analyze the progression of residents in emergency medicine training.

    Main Methods:

    • Utilized data from 23 second-year emergency medicine residents across 3 Canadian teaching hospitals.
    • Employed a multilevel regression model to analyze 1498 shift-based global performance ratings.
    • Adjusted for data clustering by resident and rater to examine performance trends over time.

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    Main Results:

    • Estimated an average baseline score of 5.7 ± 0.6, with a slight increase per assessment.
    • Demonstrated significant individual variation in residents' initial performance levels (y-intercept).
    • Revealed significant differences in the rate of performance progression (slope) among residents.

    Conclusions:

    • Residents exhibit diverse starting competencies and learning trajectories.
    • Program directors and Clinical Competency Committees should acknowledge the individualized nature of resident progression.
    • Awareness of potential 'noise' in assessment data, including rater variability, is crucial for accurate evaluation.