EDITORIAL The birth of Perspectives on Medical Education
Bibliographic record
Abstract
The Author(s) 2012. This article is published with open access at Springerlink.com Medical education has a long history in the Netherlands and Flanders. This is not only reflected by the leading role of Dutch and Flemish universities in both undergraduate and postgraduate training, but also by the high number of scientific articles in international journals. A few examples to support these introductory statements. The national Blueprint with objectives of undergraduate medical education was first published in 1994 and contained one of the first descriptions of medical programmes in the world [1]. Soon after its publication, the Dutch Ministry of Health introduced the Blueprint as the nationwide ‘gold standard ’ of medical education to be used for accreditation purposes. In 2009 the third edition of the Blueprint was published, referring to the CanMEDS competency framework as the basis for medical undergraduate training [2]. Furthermore, in the postgraduate medical specialist training, Dutch and Flemish universities appear to be enthusiastic followers of modernization task forces. The process of modernization according to the CanMEDS model is so energetic in the Netherlands and Flanders that its implementation is only a few steps behind that of our Canadian colleagues! With respect to scientific research in the area of medical education, the Netherlands is among the most active countries. In a bibliometric survey of the number of publications in international peer-reviewed journals in 2010 the Netherlands ranked number 3 after Canada and the UK. Furthermore, the list of most productive authors is led by Dutch colleagues [3]. Given the size of the country and the relatively small number of medical schools (8 in the Netherlands and 5 in Flanders), these figures characterize the prominent role of Dutch research groups in the field of medical education.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".