51 Evaluating the Impact of International Surgical Society Research Grant Funding
Bibliographic record
Abstract
Abstract Introduction Research impact is defined as the influence of research beyond academia. The European Association of Endoscopic Surgery (EAES) supports research through financial and training support. With the current project we aim to define and objectively measure the impact of research supported by one of the biggest surgical societies in Europe. Methods & Procedures A Steering Committee finalised the questionnaire through a Delphi process. This was disseminated to all previous EAES Research Grant recipients since 2011. Results were used to generate open ended questions for semi-structured interviews. Thematic analysis was performed. Results The questionnaire response rate was 22/30(73%). The median number of presentations was 2.5, highest number of citations/year was 19. Seven respondents participated on guideline committees, while 15 were involved in further research, including six multicentre trials. Three studies instigated changes in clinical policies. Two intellectual property designs were produced. Eight recipients received further funding. Experts rated a median positive impact of 5/7 on their career progression, while supervised non-experts rated 6/7. The framework from interview thematic analysis included the following topics: (i)benefits of EAES involvement (ii)career progression (academic-clinical) (iii)impact on skills (iv)network impact (v) overall impact (vi)EAES project support (vii)project challenges (viii)EAES project results (ix)study design suitability, and (x)EAES future scope. Conclusions The impact of research funding by EAES was significant; both academically and outside academia. The semi-structured interviews identified the facilitators and barriers to project progression and impact.
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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 | 0.355 | 0.586 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".