Maternal social determinants of stunting events in Kulon Progo, Yogyakarta
Bibliographic record
Abstract
Background and purpose: Kulon Progo District is one of the 100 priority districts in Indonesia for stunting program with a prevalence of 22.65%. The purpose of this research is to identify association between mother's education, family income and exclusive breastfeeding history with stunting events in Kulon Progo District, Yogyakarta.Methods: This research used a cross sectional design which conducted in Kulon Progo District, Yogyakarta. There were 729 children aged 0-59 months recorded in the integrated service post (posyandu) who met the inclusion criteria. Data collection was conducted using a questionnaire for children and mothers’ characteristics, while stunting status was retrieved from the growth monitoring card. Data analysis was conducted using Chi-Square test.Results: Children in the survey were 53.77% boys and around a quarter (24.69%) were at 25-36 months old. The majority of the mothers (72.98%) were between the ages of 26 and 35 years with high school education (67.35%) and most of them did not work (82.17%). Children with lower maternal education are less likely to experience stunting (PR=0.751; 95%CI: 0.593-0.952; p=0.013), and those from lower income family have almost five fold increased chance of stunting (PR=4.562; 95%CI: 2.643–7.874; p<0.001). Whilst for history of exclusive breastfeeding, the association was not statistically significant with stunting (PR=0.795; 95%CI: 0.529–1.196; p=0.239)Conclusion: There is a relationship between maternal and family education with the probability of stunting, while there is 20% reduced chance of stunting if children were breast fed although it was not statistically significant. Improving maternal awareness on stunting is essential for stunting management programs.
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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 | 0.000 | 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) | 0.002 | 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".