Productivity in medical education research: an examination of countries of origin
Bibliographic record
Abstract
BACKGROUND: Productivity and countries of origin of publications within the field of medical education research have not been explored. Using bibliometric techniques we conducted an analysis of studies evaluating medical education interventions, examining the country where research originated as well as networks of authors within countries identified as 'most productive'. METHODS: PubMed was used to search for evaluative studies of medical education. We then examined relative productivity of countries with >100 publications in our sample (number of publications/number of medical schools in country). Author networks from the top 2 countries with the highest relative productivity were constructed. RESULTS: 6874 publications from 18,883 different authors were included. The countries with the highest relative publication productivity were Canada (37.1), Netherlands (28.3), New Zealand (27), the UK (23), and the U.S.A (17.1). Author collaboration networks differed in both numbers of authors and intensity of collaborations in the countries with highest relative productivity. CONCLUSIONS: In terms of the number of publications of evaluative studies in medical education, Canada.
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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 | 0.054 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.074 | 0.125 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".