Upaya Percepatan Pencegahan Dan Penurunan Stunting Melalui Program Pendampingan Keluarga Di Desa Pakuan Kecamatan Narmada
Bibliographic record
Abstract
The problem of short children (stunting) is one of the nutritional problems faced in the world, especially in Pakuan village. Stunting is a problem because it is associated with an increased risk of illness and death, sobotimal brain development so that motor development is delayed and mental growth is stunted. Pakuan Village is one of the villages in Narmada sub-district which is the locus of stunting. Based on the stunting data from PSG in Pakuan village in August 2021, in Pakuan village there are 70 children who are stunted, of which 24 children are under two years old. The factors that cause stunting in the village of Pakuan include parenting patterns, lifestyle of pregnant women, unhealthy families, early marriage and there are still many parents who believe in beliefs or myths about the wrong diet of pregnant women in the community. The purpose of this activity is to accelerate efforts to prevent and reduce stunting through a family assistance program in Pakuan village, Narmada sub-district. The method is carried out by carrying out socialization activities, direct family assistance to the Pakuan village community who are the targets and monitoring through posyandu. This activity was realized through collaboration with cadres, village midwives, and the Suranadi Health Center. Based on these activities, the community can understand the pattern of parenting and nutrition given to infants/toddlers at that age in Pakuan village and there are 4 children who experience weight gain, namely Devin Alka, M. Iqbal, Iklal Fikrar, Miatul Febrianti, and Rizki Evarista.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".