Nutrition economics – characterising the economic and health impact of nutrition
Bibliographic record
Abstract
There is a new merging of health economics and nutrition disciplines to assess the impact of diet on health and disease prevention and to characterise the health and economic aspects of specific changes in nutritional behaviour and nutrition recommendations. A rationale exists for developing the field of nutrition economics which could offer a better understanding of both nutrition, in the context of having a significant influence on health outcomes, and economics, in order to estimate the absolute and relative monetary impact of health measures. For this purpose, an expert meeting assessed questions aimed at clarifying the scope and identifying the key issues that should be taken into consideration in developing nutrition economics as a discipline that could potentially address important questions. We propose a first multidisciplinary outline for understanding the principles and particular characteristics of this emerging field. We summarise here the concepts and the observations of workshop participants and propose a basic setting for nutrition economics and health outcomes research as a novel discipline to support nutrition, health economics and health policy development in an evidence and health-benefit-based manner.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".