Bibliographic record
Abstract
BACKGROUND: The medical regulator in the UK, the General Medical Council, requires curricula and assessments for postgraduate training to be blueprinted to its regulatory statement, Good Medical Practice. A similar document, Tomorrow's Doctors (2009), covers undergraduate education and training. Good Medical Practice, originally designed to regulate medical practice, is not optimally worded as an educational document. The Royal College of Physicians and Surgeons of Canada's physician competency framework known as CanMEDS is designed with education more centrally in mind. METHODS: The wordings of Good Medical Practice and Tomorrow's Doctors (2009) were compared with CanMEDS using 'word clouds', a textual analysis tool which provides a display of word frequency, revealing the emphasis in the wording of documents. RESULTS: Good Medical Practice places much greater emphasis on the regulatory rather than the educational aspects of medical practice when compared with CanMEDS and is therefore less suitable for blueprinting curricula, especially in disciplines with high science content such as pathology. CONCLUSIONS: Good Medical Practice is less suitable for an educational role and the General Medical Council should consider developing a more specific educational document around these principles.
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".