Implementation of The Convergence Program to Accelerate Stunting Reduction in Sibolga City
Bibliographic record
Abstract
Sibolga City is one of the priority cities in the effort to accelerate stunting reduction. Sibolga City implements an accelerated stunting prevalence reduction program that involves cross-sectors, better known as the convergence action to accelerate stunting reduction. The purpose of the study was to evaluate the implementation of the convergence program to accelerate stunting reduction in Sibolga City. This research is a qualitative study involving 18 informants from Regional Work Units that are members of the Sibolga City Stunting Reduction Acceleration Team. This research was conducted in November 2023 in Sibolga City, which consists of 4 sub-districts and 17 villages. Data were collected by conducting Focus Group Discussions and in-depth interviews. Furthermore, Data is analyzed through triangulation of methods, sources, and between researchers. The results of the analysis of the effectiveness of the program show that the quality of human resources and financing sources is good enough to reduce the stunting rate in the kelurahan which is the locus of stunting. Likewise, the results of the analysis of the program implementation process. The program planning that was prepared was in accordance with the problems obtained in the situation analysis and had targeted the locus villages determined in the situation analysis. The implementation and evaluation process has also been carried out well, which ultimately has an impact on reducing the stunting prevalence rate by 11.3%. As an effort to encourage a greater reduction in stunting prevalence, we recommend that the Sibolga City Government increase human resource capacity and funding, encourage the involvement of various parties including the private sector in accelerating stunting reduction programs in Sibolga City, improve coordination between Regional Work Units in the planning, implementation and evaluation processes and encourage the availability of a quality and sustainable data management system in each village Keywords: Accelerate, Convergence, Reduction, Prevalence, 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".