A quarter century of teacher training in family medicine in Europe: homage to the Bled course
Bibliographic record
Abstract
[Excerpt] If it isn’t broken, don’t fix it.’ This advice can apply to many situations. It can certainly apply to the annual Janko Kersnik International EURACT Bled Course for teacher training in family medicine. This course will convene for the 25th time in September of this year. It is worth pausing to examine the remarkable path this course has taken, to assess its influence, and to speculate on how it can continue to serve family medicine in Europe. The early history of this course has been documented in a number of publications,1-2 and additional articles planned to discuss future directions. This editorial will provide a more personal view of the course and some reflections on how the lessons learned in the course may be applied locally. The course began in 1991 as an initiative of the Slovene Association of Family Doctors and the Department of Family Medicine in Ljubljana, to prepare young teachers in Slovenia for the task of tutoring and training medical students and residents in family medicine. The profession had recently received specialist status in that country and a new cadre of teachers were required. In order to enrich the program, foreign guests, including Jaime Correia de Sousa were invited to join the faculty. The success of the first course led to the planning of a second and so it continued for twenty-five years. I have been involved annually as a course director since 1997. [...]
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".