Nutrition quality of packaged food and beverages in Costa Rica: an input for crafting harmonious school food environment policies
Bibliographic record
Abstract
The objective of this study was to compare the proportion and types of foods and beverages that would be subject to regulation according to two nutrient profiles: the Costa Rica School Decree (CRSD) and the Pan American Health Organization (PAHO) profile, and to provide recommendations for future policy design.The CRSD regulates the content of energy, sugar, total and saturated fats, and sodium, whereas the PAHO model regulates free sugar, total, saturated, and trans fats, sodium, and the presence of non-nutritive sweeteners.In this cross-sectional study, we analyzed the content of calories, sodium, sugars, and saturated fats in packaged products (n = 2,216) collected in 2015 and determined the proportion of non-compliant products according to both nutrient profiles.The agreement for non-compliant/compliant products was estimated, and the median number of nutrients in excess was compared.According to the Costa Rica School Decree, 85.2% of foods and 66.1% of beverages would be classified as non-compliant.A larger proportion of products was classified as non-compliant according to the PAHO profile (91.9% of foods and 70.9% of beverages).Chocolates and marshmallows, cookies, and crackers had the highest median number of nutrients in excess (three to four), followed by bakery items, nuts and seeds, and salty snacks (two to three).For beverages, the median number of nutrients in excess was one, according to both profiles.In conclusion, differences were found between the two nutrient profiles, which should be considered when discussing a future regulation on front-of-package (FOP) warning labels.In addition, the percentage of packaged products sold in Costa Rica that are excessive in critical nutrients, and therefore would be subject to a warning label, was high, representing a public health concern.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".