Nutrition economics – food as an ally of public health
Bibliographic record
Abstract
Non-communicable diseases (NCD) are a major and increasing contributor to morbidity and mortality in developed and developing countries. Much of the chronic disease burden is preventable through modification of lifestyle behaviours, and increased attention is being focused on identifying and implementing effective preventative health strategies. Nutrition has been identified as a major modifiable determinant of NCD. The recent merging of health economics and nutritional sciences to form the nascent discipline of nutrition economics aims to assess the impact of diet on health and disease prevention, and to evaluate options for changing dietary choices, while incorporating an understanding of the immediate impacts and downstream consequences. In short, nutrition economics allows for generation of policy-relevant evidence, and as such the discipline is a crucial partner in achieving better population nutritional status and improvements in public health and wellness. The objective of the present paper is to summarise presentations made at a satellite symposium held during the 11th European Nutrition Conference, 28 October 2011, where the role of nutrition and its potential to reduce the public health burden through alleviating undernutrition and nutrition deficiencies, promoting better-quality diets and incorporating a role for functional foods were discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".