Current Status of and Recommendations for Nutrition Education in Gastroenterology Fellowship Training in Canada
Bibliographic record
Abstract
PURPOSE: Knowledge and skill in the area of nutrition are a key competency for the gastroenterologist. However, standards for nutrition education for gastroenterology fellows in Canada do not exist, and gastroenterologists in training and in practice do not feel confident in their knowledge or skill as it relates to nutrition. This study was undertaken to identify the current status of nutrition education in gastroenterology (GI) fellowship training programs in Canada and to provide insight into the development of nutrition educational goals, processes, and evaluation. METHODS: Using mixed methods, we did a survey of current and recent graduates and program directors of GI fellowship programs in Canada. We undertook a focus group with program directors and fellows to corroborate findings of the survey and to identify strategies to advance nutrition education, knowledge, and skill of trainees. RESULTS: In total, 89.3% of the respondents perceived that the nutrition education was important for GI training, and 82.1% of the respondents perceived nutrition care would be part of their practice. However, only 50% of respondents had a formal rotation in their program, and it was mandatory only 36% of the time. Of the respondents, 95% felt that nutrition education should be standardized within GI fellowship training. CONCLUSIONS: Significant gaps in nutrition education exist with GI fellowship programs in Canada. The creation of standards for nutrition education would be valued by training programs, and such a nutrition curriculum for GI fellowship training in Canada is proposed.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".