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Record W4308725391 · doi:10.29303/jpmpi.v5i4.2415

Upaya Percepatan Pencegahan Dan Penurunan Stunting Melalui Program Pendampingan Keluarga Di Desa Pakuan Kecamatan Narmada

2022· article· en· W4308725391 on OpenAlexaff
Bq Mekia Rahmayanti, Ruth Stella Petrunella Thei, Dita Ayu Saputri, Sahru Ramdani

Bibliographic record

VenueJurnal Pengabdian Magister Pendidikan IPA · 2022
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsImmunoPrecise (Canada)
Fundersnot available
KeywordsEnvironmental healthSocializationMedicineGeographyDemographyGerontologySocioeconomicsPsychologySociologyDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.031
GPT teacher head0.326
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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