Improving nutrition in Afghanistan through a community-based growth monitoring and promotion programme: A pre–post evaluation in five districts
Bibliographic record
Abstract
In Afghanistan, malnutrition in children less than 60 months of age remains high despite nutritional services being offered in health facilities since 2003. Afghanistan's Ministry of Public Health solicited extensive community consultation to develop pictorial community-based growth monitoring and promotion (cGMP) tools to help illiterate community health workers (CHWs) provide nutritional assessment and counselling. The planned evaluation in the five districts where cGMP was implemented demonstrated that a mean weight-for-age (WFA) Z-score of 414 participant children was 0.3 Z-scores higher than that of matched non-participants who lived outside of cGMP programme catchment areas. The mean change in WFA Z-scores at evaluation was 0.3 (95% CI 0.3, 0.4) Z-scores higher than at entry into the programme. The most influential factor on WFA Z-score changes in participants was initial WFA Z-score. Those with an initial WFA Z-score of less than -2 experienced a mean increase of 0.33 (95% CI 0.29, 0.38) WFA Z-scores per session attended, while those with a baseline WFA Z-score of greater than zero showed a decrease of 0.19 (95% CI 0.22, 0.15) WFA Z-scores per session attended. These results are encouraging since they demonstrate that the cGMP programme in Afghanistan for illiterate women has some potential to contribute to improving nutrition, specifically in underweight children of either sex who enter the programme at less than nine months of age and attend 50% or more sessions.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".