The postgraduate medical education pathway: an international comparison
Bibliographic record
Abstract
An at first sight seemingly coherent, global medical workforce, with clearly recognizable specialities, subspecialties and primary care doctors, appears at a closer look quite variable. Even within the most progressive countries as to the development of medical education, with educators who regularly meet at conferences and share major journals about medical education, the differences in structures and regulations are big. This contribution focuses on the preparation, admission policy, duration, examinations, and national competency frameworks in postgraduate speciality training in Germany, the USA, Canada, the UK, Australia and the Netherlands. While general objectives for postgraduate training programs have not been very clear, only recently competency-frameworks, created in a limited number of countries, serve harmonize objectives. This process appears to be a challenge and the recent creation of milestones for the reporting on progress of individual trainees (in the US and in Canada in different ways) and the adoption of entrustable professional activities, a most recent concept that is quickly spreading internationally as a framework for teaching and assessing in the clinical workplace is an interesting and hopeful development, but time will tell whether true harmonization across countries will happen.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".