Exploring patterns and pattern languages of medical education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it