Introducing competency-based postgraduate medical education in the Netherlands
Bibliographic record
Abstract
Medical boards around the world face the challenge of creating competency-based postgraduate training programs. Recent legislation requires that all postgraduate medical training programmes in The Netherlands be reformed. In this article the Dutch Advisory Board for Postgraduate Curriculum Development shares some of their experiences with guiding the design of specialist training programs, based on the Canadian Medical Educational Directives for Specialists (CanMEDS). All twenty-seven Dutch Medical Specialty Societies take three steps in designing a curriculum. First they divide the entire content of a specialty into logical units, so-called 'themes'. The second step is discussing, for each theme, for which tasks trainees have to be instructed, guided, and assessed. Finally, for each task an assessment method is chosen to focus on a limited number of CanMEDS roles. This leads to a three step training cycle: (i) based on their in-training assessment and practices, trainees will gather evidence on their development in a portfolio; (ii) this evidence stimulates the trainee and the supervisor to regularly reflect on a trainee's global development regarding the CanMEDS roles as well as on the performance in specific tasks; (iii) a personal development plan structures future learning goals and strategies. The experiences in the Netherlands are in line with international developments in postgraduate medical education and with the literature on workplace-based teaching and learning.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".