Bibliographic record
Abstract
In preparation for a celebration of '30 years of OSCEs' held during the 2002 meeting of the Association for Medical Education in Europe (AMEE), I was asked to discuss the question, 'Are OSCEs valid to assess competence?". My first instinct was to review work undertaken in ay countries by famous researchers such as Harden, Colliver, Rothman, van der Vleuten, Stillman, Tamblyn and others who have studied and written about the validity of OSCEs. I could then have reviewed the extensive literature produced in Canada by the Medical Council of Canada and in the United States by the Education Commission on Foreign Medical Graduates and National Board of Medical Examiners that has demonstrated the utility of large-scale OSCEs for certification and licensure. I might have tossed in a few papers from my own research on the validity of OSCEs in psychiatry. Indeed, it would have been relatively easy to marshal the medical education literature to answer the question 'Are OSCEs valid to assess competence' strongly in the affirmative. But the more I reflected on the question, the more I confronted concerns that have troubled me for some time. Specifically, I worry that our approaches to validity may themselves not be valid. In this paper, I review what I believe to be three serious problems with our current approaches to showing that 'the OSCE is valid' Let me begin by rethinking the question. What do we mean 'Is the OSCE valid for assessing competence' There are three important problems with this question. First, validity is a property of the application of a test, not of a test itself. Second, we cannot speak of validity without giving consideration to the context in which we use the test. And finally, the concept of validity flounders because the OSCE itself is an important agent in constructing the variables of performance that it is designed to measure. I shall consider each issue in turn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.339 | 0.658 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".