Contribution of home garden vegetables on reducing stunting among 6‐ to 23‐month‐old children in South Tigray, northern Ethiopia
Bibliographic record
Abstract
Abstract The study was conducted to analyze the contribution of home garden vegetables in reducing stunting among 6‐ to 23‐month‐old children from South Tigray, Northern Ethiopia. The quasi‐experimental study design was used. Multistage sampling technique was used to select the districts and study communities. A total of 94 purposively selected vegetable producer (intervention) households and 260 randomly selected non‐producer (control) households were included in the study (1:3 ratio). The recumbent length of children was measured using horizontal wooden board to the nearest 0.1 cm. The length‐for‐ageZ‐scores were computed using WHO‐Anthro 2006 software. Propensity score‐matching and difference‐in‐difference (DID) estimates were used to analyze data using STATA software version 12. Prevalence of child stunting was 19.8 (12.7–29.4) and 21.1 (16.4–26.7)% (baseline) and 43.5 (33.5–54.1) and 46.5 (45.7–47.2)% (end line) among intervention and control groups, respectively. Child stunting was higher for boys and older children from both intervention and control households. DID estimation revealed that there was no significant difference in child stunting between intervention and control households (DID = 1.7,p = .604). However, there was an intervention effect of −0.5, 2.5, and 1.7% in the prevalence of child stunting among females, males, and both sexes, respectively. Vegetable production as an intervention strategy reduced the prevalence of stunting in children aged 6–23 months. However, vegetable production needs to be well integrated with other nutrition‐sensitive interventions to realize the objective of reducing child stunting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".