Efektifitas pemberian makanan tambahan (PMT) berbahan lokal terhadap berat badan dan status gizi balita: Tinjauan literatur
Bibliographic record
Abstract
Background: Nutritional problems among toddlers remain a public health challenge in many regions, including Indonesia. Locally-based Supplementary Feeding (PMT) is often utilized to address nutritional issues, particularly in improving toddler weight and nutritional status.Objectives: This article aims to evaluate the effectiveness of locally-based supplementary feeding in improving toddler weight and nutritional status through a literature review.Methods: This literature review applied a narrative review approach by searching articles in PubMed and Google Scholar (2015–2024). Articles were selected based on inclusion criteria, including RCTs and observational studies discussing the impact of local PMT on toddler weight and nutritional status. Study quality was assessed using the Newcastle-Ottawa Scale and GRADE. Data were analyzed descriptively without quantitative meta-analysis.Results: Most studies reported that local PMT effectively increased toddler weight. However, its impact on nutritional status was often insignificant within a short intervention duration. Key factors determining the success of the intervention included nutrient composition, intervention duration, and adherence level.Conclusion: Local food-based PMT is effective in improving toddler weight, although it may not significantly change nutritional status in the short term. This intervention can serve as a relevant alternative to support toddler nutrition, especially in areas with limited access to nutritious food.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".