Faktor-Faktor Keberhasilan Program Promosi Kesehatan “Gempur Stunting” Dalam Penanganan Stunting di Puskesmas Rancakalong Sumedang
Bibliographic record
Abstract
The Indonesian government has set 60 priority districts/cities for stunting handling. Based on this determination, Sumedang is included in the priority district because in 2018 the stunting prevalence rate reached 32%. The selection conducted by Bappeda Sumedang contained 10 villages where the prevalence rate of stunting was high and three of them were villages in Rancakalong. To overcome this, the Rancakalong Community Health Center initiated the “Gempur Stunting” Health promotion Program which has succeeded in reducing the prevalence of stunting from 27.7% to 19.8%, making it an exemplary health promotion program. This research was conducted to determine the success factors of the "Gempur Stunting" health promotion. The results showed that reducing the highest stunting prevalence rate in Sumedang was due to the following supporting factors: (1) variations in community-based activities; (2) Good collaboration and coordination between related sectors, and (3) Reliability of the stunting-fighting health promotion 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".