How Much do Gastroenterology Fellows Know About Nutrition?
Bibliographic record
Abstract
BACKGROUND: Many people are afflicted with chronic diseases, in which nutrition plays a key role. The need for greater nutrition training among physicians, particularly gastroenterologists, is becoming increasingly evident. OBJECTIVES: To determine the nutritional knowledge and perceived nutrition knowledge of gastroenterology fellows. METHODS: Thirty-two gastrointestinal (GI) fellows currently enrolled in a GI fellowship program completed a needs assessment evaluating perceived nutrition knowledge and interest in the areas of nutrition support, assessment, obesity, micro/macronutrients, and nutrition in GI diseases. Additionally, an examination evaluating nutrition knowledge specific to gastroenterology fellows was administered. RESULTS: Thirty-two GI fellows completed the needs assessment. Cronbach alpha of the needs assessment instrument was 0.72, indicating satisfactory internal consistency reliability. GI fellows perceived themselves to have the least knowledge in obesity and micro/macronutrients. They indicated a perceived greater knowledge base in nutrition assessment. The mean total test score was 50.04% (SD=7.84%). Fellows had the highest score in the subscale of nutrition assessment (80.64%; SD=19.05%), which was significantly higher than scores obtained in nutrition support (49.45%; SD=11.98%; P<0.05), micro/macronutrients (37.84%; SD=16.94%; P<0.05), obesity (40.11%; SD=20.00%; P<0.05), and nutrition in GI diseases (65.05%; SD=22.09%; P<0.05). A backward linear regression including hours of nutrition education received during GI fellowship, hours of nutrition education received during medical school, and year of GI fellowship accounted for 22.7% of the variance in test performance (multiple R=0.477). CONCLUSIONS: Gastroenterology fellows think their knowledge of nutrition is suboptimal; objective evaluation of nutrition knowledge in this cohort confirmed this belief. A formal component of nutrition education could be developed in the context of GI fellowship education and continuing medical education as necessary.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".