The Impact of the Central Asia Stunting Initiative on Stunting Among Children Under Five Years Old in Gilgit Baltistan and Chitral, Pakistan
Bibliographic record
Abstract
Background: Stunting, a form of chronic malnutrition, is a global health concern, especially in South Asia. Stunting remains a significant public health issue in Pakistan, particularly in remote regions like Gilgit-Baltistan and Chitral, where geographic isolation and socioeconomic challenges exacerbate malnutrition. The Aga Khan Development Network is leading the implementation of a program, Central Asia Stunting Initiative (CASI), with an aim to reduce stunting through community-driven maternal and child health interventions in the targeted areas of Gilgit Baltistan and Chitral. This study aimed to evaluate the effectiveness of CASI in improving child nutritional outcomes in Gilgit-Baltistan and Chitral. Methods: In this study, a single-group pre–post evaluation design was employed using baseline and midline cross-sectional surveys among households with children aged 0–59 months in Gilgit-Baltistan and Chitral. Data on child anthropometry, household food security, maternal education, and child feeding practices were collected from over 500 households using stratified sampling. Results: Results showed improvement in child health indicators between baseline and midline. Between baseline and midline, stunting declined from 40.9% to 35.4% in GBC (p = 0.02), with severe stunting dropping significantly (17.8% to 10.9%, p < 0.001). Wasting and underweight rates also showed marked reductions. Improvements in breastfeeding rates (71.3% to 88.3%) and dietary diversity (4.0% to 26.8%) were observed. However, food security declined sharply from 95.2% to 11.9%, underscoring persistent economic stress. Conclusions: CASI interventions yielded substantial improvements in child nutrition and maternal behaviours. However, sustained progress requires integrated strategies addressing food insecurity, economic empowerment, and long-term resilience. Future programs should adopt a multi-sectoral approach to tackle chronic malnutrition comprehensively. Despite this, results indicated an overall improvement due to CASI interventions, signifying the importance of integrated, community-based approaches in addressing 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".