Bibliographic record
Abstract
It is now widely accepted that poor nutrition plays a major role in the epidemic of various diseases, including obesity, type 2 diabetes and CVD. There has also been much research regarding the role of related factors such as advertising and food prices. Many intervention studies have been carried out where attempts have been made to persuade people to modify their behaviour, such as by making dietary changes, in order to enhance health (health promotion). There has also been much debate on the potential of government policy as a tool for achieving these goals. Various proposals have been made, such as a tax on sugary drinks, the redirection of food subsidies and how the salt content of food can be reduced. However, the great majority of previous papers have considered only single aspects of the topics discussed here. The present paper reviews strategies for improving public health, both health promotion interventions and the use of government policy approaches. Topics discussed include providing advice for the general population and the design of food guides and food labels. This leads to the conclusion that we need an overall strategy that integrates this diverse body of information and formulates a comprehensive action plan. I propose the term 'strategic nutrition'. The implementation of this plan opens up a path to a major advance in public health.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".