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Assessing professional competence: from methods to programmes
Abstract
Introduction We use a utility model to illustrate that, firstly, selecting an assessment method involves context‐dependent compromises, and secondly, that assessment is not a measurement problem but an instructional design problem, comprising educational, implementation and resource aspects. In the model, assessment characteristics are differently weighted depending on the purpose and context of the assessment. Empirical and theoretical developments Of the characteristics in the model, we focus on reliability, validity and educational impact and argue that they are not inherent qualities of any instrument. Reliability depends not on structuring or standardisation but on sampling. Key issues concerning validity are authenticity and integration of competencies. Assessment in medical education addresses complex competencies and thus requires quantitative and qualitative information from different sources as well as professional judgement. Adequate sampling across judges, instruments and contexts can ensure both validity and reliability. Despite recognition that assessment drives learning, this relationship has been little researched, possibly because of its strong context dependence. Assessment as instructional design When assessment should stimulate learning and requires adequate sampling, in authentic contexts, of the performance of complex competencies that cannot be broken down into simple parts, we need to make a shift from individual methods to an integral programme, intertwined with the education programme. Therefore, we need an instructional design perspective. Implications for development and research Programmatic instructional design hinges on a careful description and motivation of choices, whose effectiveness should be measured against the intended outcomes. We should not evaluate individual methods, but provide evidence of the utility of the assessment programme as a whole.
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“In the new movement towards programmatic assessment, progress and learning from feedback is emphasized and built into the system [74, 75].”
From Guidelines: The do’s, don’ts and don’t knows of feedback for clinical education. (2015) · cites this paper as [74]
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Metadata
- Authors
- Van Der Vleuten, C. P. M., & Schuwirth, L. W. T.
- Affiliations
- Maastricht University (NL)
- Venue
- Medical Education
- Publisher
- Wiley
- Published
- 2005-02-23
- Volume
- 39
- Issue
- 3
- Pages
- 309-317
- ISSN
- 0308-0110 (ISSN-L)
- 1365-2923
- OpenAlex
- W2163175447
- Cited by
- 1,268
- References
- 44
- Citations by year
- 2026: 87 · 2025: 46 · 2024: 51 · 2023: 66 · 2022: 66 · 2021: 92 · 2020: 63 · 2019: 76 · 2018: 59 · 2017: 66 · 2016: 65 · 2015: 75 · 2014: 72 · 2013: 64 · 2012: 47
- FWCI
- 33.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Education (publishedVersion)
- PubMed (publishedVersion)
- Northumbria Research Link (Northumbria University) (acceptedVersion)
- Topics
- Social Sciences › Education › Innovations in Medical Education
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Competency Development and Evaluation
- Keywords
- professional competence assessment, assessment methods, assessment reliability, assessment validity, sampling, authenticity, competency-based education
- MeSH terms
- Clinical Competence, Education, Medical, Undergraduate, Learning, Reproducibility of Results, Program Evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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