The Comparison of Risk Factors for Stunting in Rural and City in Lampung
Bibliographic record
Abstract
Globally, Rural areas have more stunted children (40%) than urban areas (33%). In contrast, in Indonesia, In 2010-2013, the prevalence of stunting in rural areas was higher than in urban areas at 40 0% and urban areas by 31.5%. This type of quantitative research uses Cross Sectional approach with the aim of study to compare risk factors for stunting in rural areas and Lampung City in 2022. The research subjects are mothers and toddlers 30 are rural, and 30 are in town. The analysis in this study used the independent t-test, Mann-Whitney, chi-square, and Fisher tests; the results showed a comparison of birth length, exclusive breastfeeding, birth spacing, economic status, and environmental factors to the incidence of stunting in cities and villages in 2022. There was no comparison of birth weight, breastfeeding for up to 2 years, depression status, number of children, parenting, dietary, and Nutrition Patterns During Pregnancy on Stunting Incidents in Cities and Villages. The dominant factors influencing stunting in cities and villages based on the results of multivariate analysis of Birth spacing. There is a comparative risk factor for stunting in both rural and urban areas in Lampung province. Stunting prevention efforts by preventing early marriage, increasing the ease of access to health services in peripheral/remote sites to reduce the distance to reach health facilities, and preventing the occurrence of Low Birth Weight Babies through various promotional efforts in preventive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".