Bibliographic record
Abstract
The use of evidence to inform the practice and policy of professional education in the health care sciences is taking on an increasingly important role alongside the use of more traditional types of knowledge. It is an addition to the repertoire in this and many professions that draw on social-science discipline knowledge. In the field of health care science professional education, the Best Evidence Medical Education Collaboration (BEME) leads the movement toward evidence-informed practice. It is a movement not without controversy, and lively debate on epistemological and practical issues is in progress. With publication of the first BEME Reviews in 2005, this debate will be extended. We can expect energetic and healthy commentaries on both the review process and the substantive findings. All this will make a valuable contribution to an important aspect of professional education practice and policy that is here to stay.
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 | 0.288 | 0.440 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.031 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".