Aceh Government’s Efforts to Reduce Stunting Through Rumoh Gizi Gampong Program in Indonesia
Bibliographic record
Abstract
Stunting remains a persistent public health challenge in Aceh Province, where prevalence rates continue to exceed the national average despite the implementation of various nationwide programs. This study critically examines the Rumoh Gizi Gampong (RGG) initiative in Sabang City as a locally driven response to the crisis of stunted growth. Using a qualitative case study design that incorporates interviews, observations, and document analysis, the research highlights that RGG functions not only as a nutritional intervention but also as a collaborative governance model, integrating local government, health agencies, women’s groups (PKK), and village communities. The findings revealed that Sabang City has achieved a significant reduction in stunting prevalence (from 25% in 2019 to 19.6% in 2024) through the strengthening of RGG and its integration with DAHSAT and Genaseh programs. These results emphasized the adaptability, cross-sectoral coordination, and sustainability of the program. By applying Parsons’ AGIL framework alongside collaborative governance literature, the study shows how adaptation to budget constraints, clear goal-setting, inter-agency integration, and the maintenance of socio-cultural values collectively contribute to program success. The novelty of this research lies in connecting macro-functionalist theory with community-based governance practices in Aceh, thereby offering both theoretical and practical contributions to understanding Aceh Government’s efforts to reduce stunting through the Rumoh Gizi Gampong program.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".