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Record W2549305305

A quarter century of teacher training in family medicine in Europe: homage to the Bled course

2016· article· en· W2549305305 on OpenAlexaboutno aff
John Yaphe

Bibliographic record

VenueRepositóriUM (Universidade do Minho) · 2016
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Medical educationCourse (navigation)Training (meteorology)Task (project management)PsychologyHistoryMedicineManagementEngineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

[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. [...]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.284
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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