Exploring patterns and pattern languages of medical education
Bibliographic record
Abstract
CONTEXT: The practices and concepts of medical education are often treated as global constants even though they can take many forms depending on the contexts in which they are realised. This represents challenges in presenting and appraising medical education research, as well as in translating practices and concepts between different contexts. This paper explores the problem and seeks to respond to its challenges. METHODS: This paper explores the application of architectural theorist Christopher Alexander's work on patterns and pattern languages to medical education. The authors review the underlying concepts of patterns and pattern language, they consider the development of pattern languages in medical education, they suggest possible applications of pattern languages for medical education and they discuss the implications of such use. Examples are drawn from across the field of medical education. RESULTS: The authors argue that the deliberate and systematic use of patterns and pattern languages in describing medical educational activities, systems and contexts can help us to make sense of the world, and the pattern languages of medical education have the potential to advance understanding and scholarship in medical education, to drive innovation and to enable critical engagement with many of the underlying issues in this field.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".