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. 2021 Jan 6;21(1):21.
doi: 10.1186/s12909-020-02448-z.

Competency assessment of an undergraduate program using a third-party, objective pre-post examination

Affiliations

Competency assessment of an undergraduate program using a third-party, objective pre-post examination

Lawrence Fulton et al. BMC Med Educ. .

Abstract

Background: Assessing competencies or program learning outcomes in educational programs is often a leadership challenge. This case study reports medical education program's efforts to document undergraduate competency attainment using a pre-post, third-party, objective testing service that allows for inter-university comparison, a testing service that is being adopted by some certification and accrediting bodies.

Methods: Students completed a pre-test after program acceptance and a post-test at the end of the last didactic semester (1.5 years later) just prior to their required internships. Scores and subscores were evaluated using t-tests (Holm-adjusted p-values). MANOVA models of sub-competency difference scores were also evaluated.

Results: Results indicate competency improvement for each of the 12 areas based on the n = 55 student sample, (p < .001 for all scores). These improvements were independent of ethnicity, age, gender, and grades. The average student improved by 12.85 points (95% CI of 10.52 to 15.18) with the largest improvements in strategic planning and leadership competency areas (21.30 and 18.33 percentage points, respectively).

Conclusions: The third-party pre-post has some face validity given that student performance improved after completing a related curriculum as would be expected. Congruent with earlier studies, we find that repeated testing helps document competency attainment and that a single method for assessment is insufficient. We further document limitations of this 3d-party exam.

Keywords: Competency assessment; Competency-based medical education (CBME); Peregrine testing; Pre-post testing; Quasi-experimental.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Distribution of students by race and gender
Fig. 2
Fig. 2
Distribution of age by gender (top) and by ethnicity / race (bottom)
Fig. 3
Fig. 3
95% confidence intervals for GPA based on gender and race
Fig. 4
Fig. 4
Scatterplot matrix with bivariate contour plots, histograms, kernel density estimates, and loess curve estimates of the bivariate scatterplot relationships
Fig. 5
Fig. 5
Raw test score improvements
Fig. 6
Fig. 6
Pre-post notched boxplots
Fig. 7
Fig. 7
Percentile difference scores

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