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Record W1625535145 · doi:10.25011/cim.v34i6.15891

Clinician-Scientist Trainee: A German Perspective

2011· article· en· W1625535145 on OpenAlexaffvenueabout
Dominick Bossé, Katrin Milger, Rory E. Morty

Bibliographic record

VenueClinical and investigative medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsOccupational and Environmental Medical Association of CanadaUniversité de Sherbrooke
Fundersnot available
KeywordsGermanWorkforceMedical educationGlobeCareer developmentGovernment (linguistics)Career pathTraining (meteorology)Career PathwaysPerspective (graphical)MedicinePsychologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

Clinician-scientists are particularly well positioned to bring basic science findings to the patient's bedside; the ultimate objective of basic research in the health sciences. Concerns have recently been raised about the decreasing workforce of clinician-scientists in both the United States of America and in Canada; however, little is known about clinician-scientists elsewhere around the globe. The purpose of this article is two-fold: 1) to feature clinician-scientist training in Germany; and 2) to provide a comparison with the Canadian system. In a question/answer interview, Rory E. Morty, director of a leading clinician-scientist training program in Germany, and Katrin Milger, a physician and graduate from that program, draw a picture of clinician-scientist training and career opportunities in Germany, outlining the place of clinician-scientists in the German medical system, the advantages and drawbacks of this training, and government initiatives to promote training and career development of clinician-scientists. The interview is followed by a discussion comparing the German and Canadian clinician-scientist development programs, focusing on barriers to trainee recruitment and career progress, and efforts to eliminate the barriers encountered along this very demanding but also very rewarding career path.

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)0.004
metaresearch head score (Gemma)0.096
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.096
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.021
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.638
GPT teacher head0.540
Teacher spread0.098 · 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.

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

Citations18
Published2011
Admission routes3
Has abstractyes

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