Competence-based curriculum development for general practice in Germany: a stepwise peer-based approach instead of reinventing the wheel
Bibliographic record
Abstract
BACKGROUND: Improving postgraduate medical training is one important step to attract more medical students into general practice. Keeping pace with international developments moving to competence-based curricula for general practice training, the aim of this project was to develop and implement such a curriculum in Germany. METHODS: A five-step, peer-based method was used for the curriculum development process including panel testing and a "test version" of the curriculum for the pilot implementation phase. The CanMEDS framework served as a basis for a new German competence-based curriculum in general practice training. Four curricula from European countries and Canada were reviewed and, following required cultural adaptions, key strengths from these were integrated. For the CanMEDS "medical expertise" element of the curriculum, the WONCA ICPC-2 classification of patient's "reason for encounters" was also integrated. RESULTS: Altogether, 37 participants were involved in the development process representing 12 different federal states in Germany, and including an expert advisor from Denmark. An official "test version" of the curriculum consisting of three parts: medical expertise, additional competencies and medical procedures was established. A system of self-assessment for trainees was integrated into the curriculum using a traffic light scale. Since March 2012, the curriculum has been made freely available online as a "test version". In 2014, an evaluation is planned using feedback from users of the test model as a further stage of the implementation process. CONCLUSIONS: The first German competence-based curriculum for general practice training has been developed using a pragmatic peer controlled approach and implementation is being trialed with a "test version" of the curriculum. This model project and its peer-based methodology may support competence-based curriculum development for other medical specialties both inside and outside Germany.
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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 | 0.047 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".