Lack of nutrient declarations and low nutritional quality of pre-packaged foods sold in Guatemalan supermarkets
Bibliographic record
Abstract
OBJECTIVE: To assess the presence of nutrition declarations and nutritional quality in pre-packaged food products sold in Guatemala. DESIGN: We photographed nutrition labels of pre-packaged foods. We extracted information about declaration of energy, total/saturated/trans-fats, total/added sugars and Na content (critical nutrients). We classified all products according to their degree of processing (NOVA classification) and nutritional quality (PAHO and WHO-Europe nutrient profile models). SETTING: Pre-packaged foods for sale in seven supermarkets in Guatemala City. PARTICIPANTS: This study did not involve human subjects. RESULTS: We assessed 3459 pre-packaged foods, including 80 % ultra-processed, 7 % processed and 13 % unprocessed/minimally processed foods or culinary ingredients. Nutritional information was available in 3021 products (87·3 %). Energy content was declared in 87·0 %; total fats in 86·1 %; saturated fats in 81·5 %; trans-fats in 48·9 %; total sugars in 70·3 %; added sugars in 0·5 % and Na/salt in 85·5 % of products. Insufficient nutrient information made impossible to assess nutritional quality in 36·6 and 17·1 % of products with the PAHO and WHO-Europe models, respectively. Using PAHO and WHO nutrient profiles, we found that 66·2 and 50 % of food products did not meet the model's nutritional criteria. CONCLUSIONS: A high proportion of pre-packaged foods with nutritional information available in Guatemalan supermarkets do not meet the nutritional criteria recommended by WHO and PAHO. Furthermore, a high proportion of products did not declare critical nutrients and many did not even provide any nutritional information. National regulations should consider making critical nutrient declarations (including trans-fats and sugars) mandatory for all products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".