Predictors of Stunting Among Children Under Five Year of Age in Indonesia: A Scoping Review
Bibliographic record
Abstract
The cases of stunting in Indonesian children under five years of age is become national issues. This is due to the prevalence of stunting in Indonesian children has still remained high comparing to other southeast countries, at the national level is approximately 31 %. The consequences of child stunting may give both immediate and long term and include increased morbidity and affect to child growth and development. There is evidence of some factors are known as risk factors of stunting in children globally. The aim of this review is to identify the current literature and compile the predictors that have been associated with stunting in Indonesia and where data gaps remain. A systematic search of the literature between 2010 and 2018 was conducted using PubMed, Google Scholar, Scopus, EBSCO and Clinical Key. A search of the literature was performed by using keywords: stunting, determinants, children under five year of age, factors, Indonesia. Papers were included in this review if they identify an association between child stunting and exposure to determinant factors. We include 18 articles in the final analysis that met with the criteria. The included studies indicated that there are several main predictors of child stunting: child factors (low birth weight, premature birth); maternal factors (parental short stature, parental education); infection, and breast feeding. A diverse range of contributing factors are, to varying degrees, associated with stunting, demonstrating the importance of considering how those predictors interacts with nutrition. Integrated health promotion, prevention and interventions by health care providers, communities including health cadres is needed to prevent new stunting children in Indonesia
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".