Learner-Directed Nutrition Content for Medical Schools to Meet LCME Standards
Bibliographic record
Abstract
Deficiencies in medical school nutrition education have been noted since the 1960s. Nutrition-related non-communicable diseases, including heart disease, stroke, cancer, diabetes, and obesity, are now the most common, costly, and preventable health problems in the US. Training medical students to assess diet and nutritional status and advise patients about a healthy diet, exercise, body weight, smoking, and alcohol consumption are critical to reducing chronic disease risk. Barriers to improving medical school nutrition content include lack of faculty preparation, limited curricular time, and the absence of funding. Several new LCME standards provide important impetus for incorporating nutrition into existing medical school curriculum as self-directed material. Fortunately, with advances in technology, electronic learning platforms, and web-based modules, nutrition can be integrated and assessed across all four years of medical school at minimal costs to medical schools. Medical educators have access to a self-study nutrition textbook,Medical Nutrition and Disease, Nutrition in Medicine© online modules, and the NHLBINutrition Curriculum Guide for Training Physicians. This paper outlines how learner-directed nutrition content can be used to meet several US and Canadian LCME accreditation standards. The health of the nation depends upon future physicians’ ability to help their patients make diet and lifestyle changes.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
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".