Risk factors associated with childhood stunting in Indonesia: A systematic review and meta-analysis.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In Indonesia, stunting is one of the most public health concerns. This study aims to systematically review and meta-analyze childhood stunting risk factors in the country. METHODS AND STUDY DESIGN: We did a systematic review and meta-analysis of observational (cross-sectional and longitudi-nal) studies on stunting risk factors published between 2010 and 2021 based on available publications in online databases of PubMed, ProQuest, EBSCO, and google scholar. The quality of the publications was evaluated using the Newcastle-Ottawa Quality Assessment Scale and organized according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. Publication bias was examined using Egger's and Begg's tests. RESULTS: A total of 17 studies from the literature search satisfied the inclusion criteria, with 642,596 subjects. The pooled stunting prevalence was 30.9% (95% CI 25.0%-36.8%). Children born with low birth weight (POR 2.39, 2.07-2.76), female (POR 1.05, 1.03-1.08), and did not get the deworming program (1.10, 1.07-1.12) are the primary child characteristics that contributed to stunting. Meanwhile, maternal age ≥ 30 years (POR 2.33, 2.23-2.44), preterm birth (POR 2.12, 2.15-2.19), and antenatal care <4 times (POR 1.25, 1.11-1.41) were among mother characteristics consistently associated with stunting. The primary household and community risk factors for stunting were food insecurity (POR 2.00, 1.37-2.92), unimproved drinking water (POR 1.42, 1.26-1.60), rural residence (POR 1.31, 1.20-1.42), and unimproved sanitation (POR 1.27, 1.12-1.44). CONCLUSIONS: A diverse range of risk factors associated with childhood stunting in In-donesia demonstrates the need to emphasize nutrition programs by scaling up to more on these determinants.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".