Bibliographic record
Abstract
Introducing the WGO Climate Course for Global Gastroenterology: “From Basics to Solutions” Objective Starting in March 2023 WGO is offering a 9 session webinar course, running through June 2023, whose primary objective is to provide expert foundational knowledge as related to GI environmental sustainability to train a cohort of global gastroenterologists who are passionate about driving change in their GI societies and health systems. Background and Rationale Climate change is a rapidly evolving public health crisis with significant implications for digestive health disease. The GI (and non-GI) medical community has been slow to engage on this issue but that is changing. We know from a recent survey of global GI society leadership that leaders are very concerned, but only ten percent of GI societies have an administrative structure in place, such as a climate change working group, which may help lead change within their organizations and countries. We also know from the survey that one of the main barriers to engagement on this issue is of the limited content expertise. Collectively, these considerations led us to develop this web-based course that strives to educate a cohort of global gastroenterologists who will then have sufficient knowledge and educational tools to work with their society’s education committee, and with their climate change working group as the creation of such groups becomes more commonplace. All sessions will be held at 07:00 US Central Standard time but will also be recorded and made available on the WGO website for later viewing. Continuing medical education (CME) credits will be available. More information is available here: https://www.worldgastroenterology.org/education-and-training/webinars The broad topics include: Overview of Course and Climate Change Fundamentals Climate Change and GI Health Food, Water Security, and Vulnerable Populations Adaptation, Resilience, and Industry Partnership Understanding the carbon footprint of GI care Greening Endoscopy and Reducing Waste Personal and Systems Advocacy and nursing efforts to mitigate the climate crisis Perspectives Building a brighter, sustainable, and better future The course planning committee: Desmond Leddin, Course chair (Canada) Guilherme Macedo, WGO President (Portugal) Geoffrey Metz, WGO President-elect (Australia) Bishr Omary, former President of the American Gastroenterological Association (USA) Mai Ling Perman, Co-Director of WGO Suva Training Center (Fiji) Andrew Veitch, President of the British Society of Gastroenterology (UK)
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.102 | 0.057 |
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".