Report of the MEDINE2 Bachelor of Medicine (Bologna First Cycle) Tuning Project
Bibliographic record
Abstract
BACKGROUND: European Higher Education institutions are expected to adopt a three-cycle system of Bachelor, Master and Doctor degrees as part of the Bologna Process. Tuning methodology was previously used by the MEDINE Thematic Network to gain consensus on core learning outcomes (LO) for primary medical degrees (Master of Medicine) across Europe. AIMS: The current study, undertaken by the MEDINE2 Thematic Network, sought to explore stakeholder opinions on core LO for Bachelor of Medicine degrees. METHOD: Key stakeholders were invited to indicate, on a Likert scale, to what extent they thought students should have achieved each of the Master of Medicine LO upon successful completion of the first three years of university education in medicine (Bachelor of Medicine). RESULTS: There were 560 responses to the online survey, representing medical students, academics, graduates, employers, patients, and virtually all EU countries. There was broad consensus between respondents that all LO previously defined for primary medical degrees should be achieved to some extent by the end of the first three years. CONCLUSIONS: The findings promote integration of undergraduate medical curricula, and also offer a common framework and terminology for discussing what a European Bachelor of Medicine graduate can and cannot do, promoting mobility, graduate employability and patient safety.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".