Umfrage zur Weiterbildung Orthopädie/Unfallchirurgie
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: A high-quality advanced training is a key factor for good and safe patient treatment. Germany is currently revising the advanced training curricula and logbooks aiming to change the training into a competence-based training. The aim of this study was to analyze the day to day reality of orthopedic and trauma surgery advanced training in Germany based on the elements of the advanced training. METHODS: In March 2020 an online survey on advanced training was carried out with 44 questions on the topics of advanced training curriculum, logbook, educational resources, evaluation, authorized trainer and distribution of working time . RESULTS: A total of 237 persons completed the survey, of which 208 fulfilled the inclusion criteria. The respondents perceived a lack of clear standards in the advanced training curriculum and 25% did not receive structured learning resources in the form of simulations or courses. Mandatory annual process interviews were performed in only 58%. Most respondents valued the expertise of the trainers in orthopedic and trauma surgery, whereas they rated their competence in supervision and giving feedback as below average. Administrative work consumed 220 min of the daily working time and on average 60min remained per day for respondents to learn operative skills. CONCLUSION: The survey revealed inconsistencies in the current advanced training curriculum and a lack of supervision and evaluation. The implementation of competence-based advanced training should therefore not only focus on a change of the curriculum but also on implementing competence-based training at all levels of training (learning resources, training, evaluation).
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".