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Record W3083393866 · doi:10.36565/jab.v9i2.274

Faktor-Faktor yang berhubungan dengan Kejadian Stunting pada Balita Usia 24-59 Bulan dari Keluarga Petani di Wilayah Kerja Puskesmas Gunung Labu Kabupaten Kerinci

2020· article· en· W3083393866 on OpenAlexaff
Asparian Asparian, Enda Setiana, Evy Wisudariani

Bibliographic record

VenueJurnal Akademika Baiturrahim Jambi · 2020
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineEnvironmental healthIncidence (geometry)Logistic regressionPopulationFood securityPediatricsGeographyAgriculture

Abstract

fetched live from OpenAlex

Background : Stunting is a state of height index according to age under -2 SD according to WHO standards. Nutrition problems in farmers can occur due to poverty which is the root of nutrition problems. The purpose of this study was to determine the factors associated with the incidence of stunting in children aged 24-59 months from farming families in the Gunung Labu Primary Health Care in Kerinci Regency. Method :The design of this study was cross sectional. The total population in this study was 1,422 toddlers, while the sample in this study was 98 toddlers from farming families. Analysis used the Chi-Square test and Multiple Logistic Regression. Result :This study found the prevalence of stunting in infants 32.34%. Factors related to the incidence of stunting in infants were household level food security and mother's education level. The most dominant factor related to the incidence of stunting in infants was household-level food security (OR = 4,722; 95% CI = 1,599-13,941). Households ware encouraged to use home yards as a place to meet food needs and provide a variety of foods and balanced nutrition for infants so that nutritional needs can be met.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.284
Teacher spread0.241 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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