Identifying best practices in junk food taxation and other food policies: Selected examples and their assessment
Bibliographic record
Abstract
Abstract Issue/problem Noncommunicable diseases are the main contributor to the global mortality, being responsible, as estimated, for 71% of deaths each year. About 80 of dietary dependent diseases has been identified so far, and their prevalence tends to exceed 30% in some populations. Description of the problem The growing prevalence of these diseases, along with impact on the quality of life, disabilities, as well as rising direct and indirect economic costs, constitute a basic foundation for emerging efforts to develop and implement new solutions within national health policies aimed at modifying dietary behaviours and reducing their negative impact on health status of individuals and populations. To address these problems the Joint Funding Action “Effectiveness of existing policies for lifestyle interventions - Policy Evaluation Network (PEN)” has been initiated, in which 28 research group across Europe are collaborating. Results The primary aim of the presented study will be to discuss the usage of the PEN instrument to identify best practices in food policies, including sugar sweetened beverages and junk food taxation. Previous examples of the instrument implementation will also be presented, including New Zealand, Australia and Canada, along with the results of current works on its development within the project. The second basic aspect for the study is to discuss and assess examples of food policies implemented in selected countries in terms of their effectiveness in modifying unhealthy behaviours. Lessons The time that has passed since the implementation of the evaluated solutions is too short to assess actual impact on health. Nonetheless, the existing evidence, including data from countries being the most successful examples of junk food taxation, like Mexico and Hungary, suggest that their impact on consumer choices, health literacy and also food industry in terms of food products composition, is positive.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".