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Record W4392208384 · doi:10.33086/cdj.v7i3.5175

Monitoring Child Growth and Development in Families at Risk of Stunting Using the Elsimil (Elektronik Siap Nikah dan Hamil) Application

2023· article· en· W4392208384 on OpenAlexaff
Nikmatur Rohmah, Hendra Kurniawan, Indah Savitri, Untung Kuzairi, Jauhari Ahmad Febriansyah, Izza Afkarina, Nova Risma Ramadhani, Audrey Amalia Shakira Maghfiro

Bibliographic record

VenueCommunity Development Journal · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSociologyPsychology

Abstract

fetched live from OpenAlex

Bondowoso Regency have 14 priority communities by 2023 to accelerate stunting reduction, and Tamanan District is host to three of the fourteen villages. The objective of this community service is to implement the Elsimil application to track the growth and development of children from families at risk of stunting. This activity is an implementation of the Contextual Learning Model for Building Villages which integrated into the Pediatric Nursing Course. The team consists of 2 pediatric nursing lecturers and 34 nursing students. The methods used including monitoring children's growth and development, performing analyses, and educating families. Interviews, weight and height checks, developmental checks, and examination of family card records and KIA books are data collecting methods which then entered into the Elsimil program. This activity's population of interest is 31 families with children under the age of two in Tamanan District, devided into 3 villages: 9 children in Sumber Anom village, 11 children in Sumber Kemuning village, and the remaining 11 children in Kemirian village. This community service held in July-August 2023. Descriptive analysis is used in data analysis. Based on weight per age, 3.2% were severely underweight, 6.5% were underweight, 87.1% were normal weight, and 3.2% were at risk of being overweight. According to body length per age, 29% were severely stunted, 19.4% were short, and 51.6% were normal. A calculation of body weight per body length, 6.5% are extremely wasted, 3.2% are wasted, 64.5% are normal, 6.5% are at risk of being overweight, 3.2% are overweight, and 16.1% are obese. The results obtained based on body mass index per age were 3.2% severely wasted, 3.2% wasted, 64.5% good nutrition (normal), 16.1% at risk of overweight, 3.2% overweight, and 9.7% obesity. We conclude that this community service could optimize children monitoring and development by assisting stunted families through Elsimil application.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.039
GPT teacher head0.288
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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