Preschoolers in severely food insecure Guatemalan Mayan households consume a monotonous grain-based diet
Bibliographic record
Abstract
BACKGROUND: Stunting, also known as growth retardation, is a prominent health challenge for Mayan children, stemming from the complex interaction of malnutrition, inflammation, limited healthcare access, poverty, and food insecurity. Diet plays a key role in early childhood growth. A monotonous diet may lack essential nutrients, potentially affecting growth and developmental trajectories. Our objective was to explore the dietary patterns of Guatemalan Mayan children, comparing stunted children to those with typical growth. METHODS: We conducted a cross-sectional study involving 155 Mayan children aged 2 to 5 years in Guatemala. Dietary intake was assessed using three 24-hour recalls. A questionnaire assessed WASH (water, sanitation, and hygiene) service availability and household food insecurity via the Household Food Insecurity Access Scale (HFIAS). Additionally, we measured weight, height, age, body composition, and recent common childhood communicable diseases (CCCD). The dietary data was analyzed based on two approaches: The nutrient approach yields the prevalence of inadequacy, and A-posteriori approach using K-means cluster analysis informed about the dietary pattern of the sample, including types of foods, and their energy contribution to the diet. RESULTS: Stunting affected 53.5% of the children, and nearly half of the children (49.7%) had experienced one or more recent CCCD. The majority resided in food-insecure households (81.9%), with more than half located in rural areas (61.6%). They had a low intake of omega-3, omega-6 fatty calcium and choline. We found two distinct dietary patterns: the grain-based diet, represented by high intakes of cereal-based drinks and corn-based meals. The second was a dairy, poultry, and corn dietary pattern; distinguished by higher consumption of dairy products and poultry. CONCLUSION: Significantly more children living in severely food insecure households consumed a grain-based dietary pattern. The stunted children on the grain-based diet consumed more calories from grain-based drinks than any other food group. Beyond dietary monotony and poor nutritional quality, additional factors likely contribute to the high stunting rate, requiring further investigation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".