Faktor-Faktor yang berhubungan dengan Kejadian Stunting pada Balita Usia 24-59 Bulan dari Keluarga Petani di Wilayah Kerja Puskesmas Gunung Labu Kabupaten Kerinci
Bibliographic record
Abstract
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".