Bibliographic record
Abstract
Clinician-scientists are particularly well positioned to bring basic science ndings 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. e 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 Ger-many, outlining the place of clinician-scientists in the German medical system, the advan-tages and drawbacks of this training, and government initiatives to promote training and career development of clinician-scientists. e interview is followed by a discussion com-paring the German and Canadian clinician-scientist development programs, focusing on barriers to trainee recruitment and career progress, and eorts to eliminate the barriers
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.883 | 0.663 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".