Effects of a participatory agriculture and nutrition education project on child growth in northern Malawi
Bibliographic record
Abstract
OBJECTIVE: To investigate whether children in households involved in a participatory agriculture and nutrition intervention had improved growth compared to children in matched comparable households and whether the level of involvement and length of time in the project had an effect on child growth. DESIGN: A prospective quasi-experimental study comparing baseline and follow-up data in 'intervention' villages with matched subjects in 'comparison' villages. Mixed model analyses were conducted on standardized child growth scores (weight- and height-for-age Z-scores), controlling for child age and testing for effects of length of time and intensity of village involvement in the intervention. SETTING: A participatory agriculture and nutrition project (the Soils, Food and Healthy Communities (SFHC) project) was initiated by Ekwendeni Hospital aimed at improving child nutritional status with smallholder farmers in a rural area in northern Malawi. Agricultural interventions involved intercropping legumes and visits from farmer researchers, while nutrition education involved home visits and group meetings. SUBJECTS: Participants in intervention villages were self-selected, and control participants were matched by age and household food security status of the child. Over a 6-year period, nine surveys were conducted, taking 3838 height and weight measures of children under the age of 3 years. RESULTS: There was an improvement over initial conditions of up to 0·6 in weight-for-age Z-score (WAZ; from -0·4 (sd 0·5) to 0·3 (sd 0·4)) for children in the longest involved villages, and an improvement over initial conditions of 0·8 in WAZ for children in the most intensely involved villages (from -0·6 (sd 0·4) to 0·2 (sd 0·4)). CONCLUSIONS: Long-term efforts to improve child nutrition through participatory agricultural interventions had a significant effect on child growth.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".