Novel method to achieve price-optimized, fully nutritious, health-promoting and acceptable national food baskets
Bibliographic record
Abstract
Purpose: The purpose of this study was to generate a framework for the development of health-promoting, fully nutritious, socially acceptable, and affordable national food baskets to be used as an advocacy tool by governments. In addition to containing all (micro-)nutrient requirements, food baskets should also reflect dietary guidelines to prevent non-communicable diseases and be optimized to achieve the highest possible social acceptance. So far, integrative approaches that include all these aspects are lacking. Methods: Food composition, local availability, food prices, national and international recommendations on ‘healthy’ nutrition, and current respective preferences of the corresponding populations were optimized using linear programming (LP) methods (Dantzig’s simplex algorithm). The desired outcomes were ‘best-for-price’ solutions (= food baskets) from a list of 150-190 locally available foods. The study was designed to obtain healthy, affordable, and socially acceptable diets for three European countries (Denmark, Slovenia, and Romania) and in three regions within Canada, Argentina, and Switzerland. Moreover, the costs for the “limiting” micronutrients and relative price increases were calculated after including a range of constraints (e.g. dietary recommendations vs nutrient requirements; wider range of biodiversity (increased number of foods included) and social acceptability). All data were collected in the respective countries using standard methodology. Results: Key micronutrients influencing the increased cost of food baskets were calcium, potassium, and the vitamins A, B2, C, and D. When additional constraints were applied by integrating food-based dietary guidelines and social acceptability (as measured by current consumption patterns, central 80% percentile), the cost increased by approximately one third and three fold, respectively. The wider range of biodiversity resulted in just minor increases in cost. Conclusions: The cost of health-promoting, fully nutritious, and socially acceptable food baskets depended primarily on their adaptation to local dietary patterns. Fully nutritious and health-promoting food baskets can be achieved at a relatively low price. However if these are not socially acceptable to the target population, the use of a framework using linear programming based solely on nutritional values seems limited.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".