STRATEGI PERCEPATAN DALAM PENURUNAN STUNTING DI PERDESAAN MELALUI OPTIMALISASI DASHAT (STUDI KASUS CEGAH STUNTING)
Bibliographic record
Abstract
Stunting remains a strategic issue in human resource development in Indonesia, especially in rural areas with limited access to nutrition, health, and sanitation services. Through the Healthy Kitchen to Overcome Stunting (DASHAT) Program, the government seeks to optimize community empowerment in meeting family nutritional needs based on local foods. This community service article aims to describe strategies to accelerate stunting reduction through the optimization of DASHAT implementation as a form of cross-sector collaboration in promoting nutritional and health security in rural communities. Activities were carried out through a participatory and educational approach involving the community, posyandu cadres, and health workers, with a focus on increasing nutritional knowledge, healthy food processing, and strengthening the role of families in preventing stunting. The results of the community service show that optimizing the DASHAT program can increase community understanding of balanced nutrition, expand the use of local food ingredients, and strengthen clean and healthy living behaviors (PHBS). The implementation of the program also encourages the formation of a collaborative network between local governments, the private sector, and the community in accelerating the sustainable reduction of stunting. Thus, the strategy to accelerate the reduction of stunting through the optimization of DASHAT is effective in increasing awareness, participation, and family resilience to chronic nutritional risks in children under five in rural areas.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".