Virtual Education is the Need of the Hour for the Global Gastroenterology Community: A Survey of Leaders of Professional Gastroenterology Organizations
Bibliographic record
Abstract
BACKGROUND AND AIMS: Since the onset of the Coronavirus disease 2019 (COVID-19) pandemic, there has been a significant opportunity to leverage virtual platforms for communication and dissemination of knowledge. An online survey was conducted to examine the viewpoints of World Gastroenterology Organization (WGO) leaders concerning the necessity, primary priority areas, and implementation strategies for a virtual global gastroenterology educational program. METHODS: We conducted a survey of leaders of WGO member societies to assess their opinions on creating opportunities for global education using virtual platforms, identifying practical implementation steps and priority educational areas. RESULTS: Responses were obtained from 57/117 (48.7%) contacted leaders with 56/57 (98.2%) identifying such a need. Five mutually exclusive priority educational topics were proposed in the survey: clinical gastroenterology, endoscopy, nutritional support, research methodology, and professional development. Overall, most participants prioritized clinical gastroenterology (45/57; 78.9%) and endoscopy/hand skills (27/57; 47.3%) as educational topics to be addressed by the virtual global gastroenterology educational program. A majority of WGO member society leaders surveyed favored monthly teaching activities (33/57; 57.8%), ideally carried out between 1500-2100 local time (31/57; 54.3%), ideally with no administrative fees (47/57; 82.4%). CONCLUSIONS: This truly global survey of WGO member societies achieved a good response rate and provides important insights into the need for and scope of future virtual education programs under the aegis of the WGO.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".