Prelacteal feeding practices with stunted in infants
Bibliographic record
Abstract
Indonesia still has a high prevalence of stunting compared with several other Southeast Asian countries. The National Survey on Nutritional Status in Indonesia in 2022 reported that stunting in Indonesia was 18.5%, while Bangka Regency recorded a stunting prevalence of 16.2%. Feeding under the age of six months has become one of the factors of stunting. This requires concrete effort to handle the problem of stunting. This study aimed to assess the association between prelacteal feeding practices and infant stunting. The research used a cross-sectional design in the Kenangan Health Centre area, Bangka Regency, in May 2023. A sample of 173 infants aged 0-6 months was obtained using cluster random sampling. The height of the selected infants was measured using a lenghtboard and an infantometer. Interviews related to practical feeding were conducted with the parents using a questionnaire. Data were analyzed using Pearson’s correlation test with α= 0,05. The results showed that 14,5% of mothers gave prelacteal food to their infants until 6 months of age. There was a relationship between prelacteal feeding practices and stunting in the Kenanga Health Center working area (p= 0,001; r= -0,663). There was a negative correlation between prelacteal feeding practices and stunting. In conclusion, there is a significant negative relationship between prelacteal feeding practices and infant stunting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".