Food security and household food expenditure in Guatemala (268.4)
Bibliographic record
Abstract
In Guatemala malnutrition and food insecurity are highly prevalent. Distinguishing between food security levels can help improve understanding of dietary patterns among specific groups, thus allowing for more effective and specialized designs of food assistance programs. The Escala Latinoamericana y Caribena de Seguridad Alimentaria (ELCSA) is a validated tool that distinguishes food secure households from three food insecurity levels: mild, moderate and severe. The purpose of this study was to assess expenditure on food by food security level in 13,482 Guatemalan households in the 2011 Living Conditions Survey (ENCOVI), in order to identify potential nutritional gaps in the given groups. Information from the 115 items survey section “Expenses and Consumption of Food” and the food security section containing ELCSA was used. Analyses were made regarding food items purchased during the twelve months previous to the survey. One way ANOVA was used to compare mean differences among food security categories on expenditures made for various food groups. Bonferroni post‐hoc analyses were used for multiple comparisons between groups with a confidence level set at 95%. Food secure households had significantly higher expenditures on meats, dairy, fruits, and vegetables when compared to food insecure groups. Severely food insecure households had a higher expenditure on staple grains and sugar, and the proportion of expenditure was significantly higher for starches, sweets, and non‐meat proteins (eggs and beans). As food insecurity level increases, expenditure on boneless beef decreases, while expenditures on chicken interiors increase. Different food consumption by food insecurity level indicates the need for differentiated interventions and programs.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".