Food insecurity and the double burden of malnutrition in Colombian rural households
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine in Colombian rural households the association between different severity levels of household food insecurity and the presence of the double burden of malnutrition (SCOWT), defined as the coexistence of a stunted child under 5 years of age and an overweight or obese (OWOB) mother. DESIGN: A secondary data analysis was conducted using cross-sectional data from the Colombian National Nutritional Survey (ENSIN) 2015. Household food insecurity status was assessed by using the Latin-American and Caribbean Food Security Scale (ELCSA). The household SCOWT status (child stunting and OWOB mother) was determined using anthropometric data from a mother and her child. SETTING: Rural Colombia. PARTICIPANTS: Totally, 2·350 mother-child pairs living in the same household. RESULTS: Sixty-two per cent of the households were food-insecure and SCOWT was present in 7·8 % of the households. Moderate (OR: 2·39, 95 % CI (1·36, 4·21)) and severe (OR: 1·86, 95 % CI (1·10, 3·15)) food insecurity was associated with SCOWT in an unadjusted logistic regression. Only moderate food insecurity remained significantly associated with SCOWT in a multivariate logistic regression (adjusted OR: 2·41, 95 % CI (1·24, 4·68)). CONCLUSIONS: Colombian rural areas are not exempt from the worldwide concern of increasing OWOB rates while stunting is still persistent. These results highlight the need of implementing double-duty rural actions targeting the most vulnerable households to SCOWT, particularly in terms of overcoming food insecurity beyond hunger satisfaction to prevent all forms of malnutrition.
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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 | 0.000 | 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) | 0.002 | 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".