Replacing ultra-processed foods with fresh foods to meet the dietary recomendations: a matter of cost?
Bibliographic record
Abstract
The study aimed to analyze the economic impact of the adoption of optimized and nutritionally balanced diets to Brazilian families, considering the Brazilian dietary guidelines and the economic disparities of the population. Data from the Brazilian Household Budget Survey from 2008-2009 (550 strata; 55,970 households) were used. About 1,700 foods and beverages purchased by the Brazilians were classified into 4 groups according to NOVA system. Linear programming models estimated isoenergetic diets preserving the current diet as baseline and optimizing healthier diets gradually based on the "golden rule" of the Brazilian dietary guidelines, respecting nutritional restrictions for macronutrients and micronutrients (based on international recommendations) and food acceptance limits (10th and 90th percentiles of the per capita calorie distribution from the population). The diet cost was defined based on the sum of the average cost of each food group, both in the current and optimized diets (BRL per 2,000Kcal/person/day). The economic impact of the Brazilian dietary guidelines to Brazilian household budget was analyzed by comparison the cost of the optimized diets to the cost of the current diet, calculated for the total population and by income level. Three healthier diets were optimized. Current diet cost was BRL 3.37, differed among low- and high-income strata (BRL 2.62 and BRL 4.17, respectively). Regardless of income, diet cost decreased when approaching the guidelines. However, low-income strata compromised their household budget more than two times the high-income strata (20.2% and 7.96%, respectively). Thus, the adoption of healthier eating practices can be performed with the same or lower budget.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".