The organisation and needs of young sections belonging to UEG National Societies: Results of a Europe‐wide survey
Bibliographic record
Abstract
One of the aims of the Young Talent Group (YTG) is to make United European Gastroenterology (UEG) more attractive for young fellows interested in gastroenterology, and to involve them actively in UEG activities, by collaborating with young GI sections (YGIS) across Europe. Therefore, the YTG launched a survey to collect up-to-date information on YGISs belonging to UEG National Societies. The Friends of YTG were chosen as the target population and received a web-based questionnaire concerning their personal information, the structure of YGIS in their respective country, the YGIS' support mechanisms for young trainees, and ideas on how to improve them. Overall, 24 of 29 Friends answered the survey (83%). Among the Societies surveyed, only half have a young section. Typically, YGIS are supported, but not influenced, by National Societies through several initiatives. Results of the survey suggest that a lack of funding, of harmonised education, and of active roles available within National Societies, were the concerns most prevalent among young fellows. Our survey shows that the development of YGIS is being hindered by organisational, financial, and political issues. The YTG believes that a close collaboration between National Societies, UEG, and the YTG is necessary in order to offer young fellows the most productive and professionally satisfying future possible.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".