Standardized evaluation of satisfaction within urology residents during clinical training: Implementation of a new urological residency rotation program at the university hospital Frankfurt
Bibliographic record
Abstract
Background: Structured curricula are demanded to improve training programs of future urologists. This study aimed to evaluate the acceptance of the newly implemented residency rotation program at the University Hospital Frankfurt. Primary endpoint was resident's satisfaction with the current residency rotation program. Secondary endpoint was the fulfilment of the objectives and expectations by residents. Methods: A standardized 15-item, online-based survey was sent to every urologic resident of the University Hospital Frankfurt, completing their rotation between August 2020 and August 2022. In addition to baseline characteristics, training and working conditions were assessed. Descriptive statistics were applied. Results: In total 15 rotations of the Residency Rotation Program at the University Hospital Frankfurt were evaluated, including urologic practice (5/15), Intermediate Care Unit (4/15), urooncology (4/15) and clinical exchange to St. Gallen (2/15). Overall, the majority were very (67%) or rather satisfied (2%) with their rotation. Of the pre-rotation defined objectives, 71% were fulfilled, 18% partially fulfilled and 8% not fulfilled. With respect to the expectations, 67% were fulfilled, 19% partly fulfilled and 4% were not fulfilled. All residents would recommend their respective rotations. Conclusion: Our results demonstrate that the residency rotation program at the University Hospital Frankfurt enjoys a high level of acceptance as well as a positive impact on urologic training. Satisfaction with the completed rotation was convincing, most of the expectations and objectives for the respective rotation could be fulfilled. These results help to ensure the quality of urologic curricula and to improve the structure of training programs for future urologists.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".