<i>Food Information Programs:</i> A Review of the Literature
Bibliographic record
Abstract
This paper summarizes existing evidence on the impact of food information programs. Published and unpublished literature produced within the past decade was searched and reviewed. Relevant data were tabulated and key findings summarized. Food information programs are becoming increasingly popular as tools to help consumers select a healthy diet. The key feature of a food information program is a package logo on foods meeting nutrition criteria set by the program s administering body. The logo acts as a health message. Several countries, including Canada, have adopted food information programs. Critics believe that these programs oversimplify the concept of healthy eating, that consumers misinterpret the logo s meaning, that licensing fees prohibit small companies from participating, and that the programs are limited to purchase behaviour and do not necessarily have an impact on dietary intake. Consumers report support for the programs and are able to interpret a logo s meaning accurately. In addition, evidence shows the programs have had a positive impact on the nutrient composition of foods. Research is still needed, however, to establish the impact of such programs on food purchases and dietary intake, and the overall and long-term effectiveness of the programs as a nutrition intervention.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".