EFEKTIVITAS METODE KELAS MEMASAK BAGI IBU BALITA UNTUK MENCEGAH STUNTING DI DESA JIPANG KECAMATAN KARANGLEWAS
Bibliographic record
Abstract
Jipang is one of the villages that belong to the working area of Karanglewas Community Health Center (Puskesmas) with a great number of stunting cases. The prevalence of stunting cases in Jipang village was 35 cases (16.13%) out of a total 217 toddlers. One of the main factors contributing to the high cases of stunting in Jipang village is the lack of knowledge and skills among mothers in taking care of their children. Many mothers have limited knowledge of parenting, particularly in terms of providing their children with proper nutrition. Besides, due to limited educational media, mothers are less informed about good parenting. In order to prevent the rising number of stunting cases, there is a need to improve mothers’ knowledge and skills in providing their children with good parenting. The aim of this activity was to improve mothers’ knowledge and skills in providing their children with good parenting. This activity consisted of a number of stages including 1) Writing a recipe book about food preventing stunting, 2) Creating leaflet and educational videos, 3) Educating mothers about stunting and proper nutrition, 4) Conducting a cooking class, and 5) Supervising a group of mothers. The result of this activity indicated that there was an improvement in terms of mothers’ knowledge and skills after the activity. Specifically, the recipe book was very useful for mothers in providing proper nutrition for their children.
 Key words: Stunting, Proper Nutrition, Cooking Class
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".