Nutrition in the medical curriculum: A vital missing ingredient
Bibliographic record
Abstract
Physicians, and in particular, general practitioners (GPs), are often the first point of contact for patients into the health care system.Patients confide in their GPs -they trust that they are competent to recognize, diagnose, treat and provide long-term management for their conditions, and they hold their advice in high regard.1 Physicians are also viewed as trusted and reliable sources of nutrition information, with the expectation that they can provide accurate information.2,3 Despite this expectation and physicians' recognition of the importance of nutrition to health and disease progression, many do not feel equipped to address their patient's nutritional needs.4-8 A Canadian study demonstrated that more than 80% of physicians believed their nutrition training was inadequate, including in medical school.8 A recent systematic review also showed that these knowledge deficits in nutrition impeded physician's confidence when delivering nutrition information to patients.9 Given the importance of nutrition in chronic disease progression, management, and prevention, it is important to highlight this gap in the medical curriculum, with the goal of improving nutrition education in medical schools to optimize patient care.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".