Workshop Report: concepts and methods in the economics of nutrition – gateways to better economic evaluation of nutrition interventions
Bibliographic record
Abstract
Improving health through better nutrition of the population may contribute to enhanced efficiency and sustainability of healthcare systems. A recent expert meeting investigated in detail a number of methodological aspects related to the discipline of nutrition economics. The role of nutrition in health maintenance and in the prevention of non-communicable diseases is now generally recognised. However, the main scope of those seeking to contain healthcare expenditures tends to focus on the management of existing chronic diseases. Identifying additional relevant dimensions to measure and the context of use will become increasingly important in selecting and developing outcome measurements for nutrition interventions. The translation of nutrition-related research data into public health guidance raises the challenging issue of carrying out more pragmatic trials in many areas where these would generate the most useful evidence for health policy decision-making. Nutrition exemplifies all the types of interventions and policy which need evaluating across the health field. There is a need to start actively engaging key stakeholders in order to collect data and to widen health technology assessment approaches for achieving a policy shift from evidence-based medicine to evidence-based decision-making in the field of nutrition.
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 | 0.134 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".