A Multidisciplinary and Formal Nutrition Education is Needed in Graduate Medical Education
Bibliographic record
Abstract
Introduction A needs assessment survey was constructed to determine faculty resources and commitment for nutrition education from Graduate Medical educators. The null hypothesis was all specialties had insufficient faculty resources to educate about current nutritional practice. Methods and A needs assessment was sent in a survey format of 25 questions to 495 ACGME Residency Program Directors in Anesthesia, Family Medicine, Medicine, Pediatrics, Obstetrics and Gynecology and Surgery. There was a 14% response rate (72 programs), consistent with similar studies on the topic. Results The majority of respondents were primary care programs (32 Family Medicine, 23 Medicine and one Pediatric). Most program directors had a fair knowledge of nutrition themselves, with 10% reporting expert knowledge, and 41 programs did not identify an expert. The majority of experts were often not considered formal Graduate Medical educators (MDs = 37, RD = 38, Pharm D= 13, RN= 12 and PhD =7). Nutrition education occurs informally, by non‐MD providers as only 19 residencies had a formal nutrition course. Seventy seven percent of residencies felt they did not meet the required educational goals for and 72% felt a national curriculum would overcome this obstacle. Conclusions There is indeed a dearth of nutrition education in current graduate medical education. Most programs lack the expertise/commitment to teach a formal course but recognize the need to meet educational requirements. Most programs rely on non‐MD multidisciplinary experts to teach residents. A broad based, diverse specialty educational program taught by MDs and other non‐MD experts is needed for training in nutrition during residency.
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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 | 0.005 | 0.022 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".