Nutrition Education for Pediatric Gastroenterology, Hepatology, and Nutrition Fellows
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to assess the methodology and content of nutrition education during gastroenterology fellowship training and the variability among the different programs. METHODS: A survey questionnaire was completed by 43 fellowship training directors of 62 active programs affiliated to the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition, including sites in the United States, Canada, and Mexico. The data were examined for patterns in teaching methodology and coverage of specific nutrition topics based on level 1 training in nutrition, which is the minimum requirement according to the published North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition fellowship training guidelines. RESULTS: The majority of the teaching was conducted by MD-degree faculty (61%), and most of the education was provided through clinical care experiences. Only 31% of the level 1 nutrition topics were consistently covered by >80% of programs, and coverage did not correlate with the size of the programs. Competency in nutrition training was primarily assessed through questions to individuals or groups of fellows (77% and 65%, respectively). Program directors cited a lack of faculty interested in nutrition and a high workload as common obstacles for teaching. CONCLUSIONS: The methodology of nutrition education during gastroenterology fellowship training is, for the most part, unstructured and inconsistent among the different programs. The minimum level 1 requirements are not consistently covered. The development of core curriculums and learning modules may be beneficial in improving nutrition education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".