Pemetaan Faktor Sosial-Ekonomi Penyebab Stunting di Kabupaten Aceh Timur
Bibliographic record
Abstract
The objective of the research is to identify and map socioeconomic factors that influence stunting rates in children in East Aceh Regency. The analytical method utilized is descriptive qualitative, utilizing interview techniques. The interviewees for this study were nutrition officers from community health centers located in each sub-district of East Aceh Regency. According to the study's findings, the factors that contribute to stunting are 1) a lack of parental understanding of the risk of stunting and the period of the First 1000 Days of Life, which has an impact on poor consumption patterns during pregnancy and parenting patterns for children, 2) family economic factors or low income levels among parents, and 3) a lack of access to health services. The limitation of this research is that researchers were unable to access all community health centers in East Aceh Regency due to logistical problems. It is hoped that future researchers would be able to conduct study in all health facilities in every sub-district of East Aceh Regency in order to create a map of the causes that cause stunting, which will be valuable in developing appropriate strategies for conquering stunting in the region
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".