Contextual Determinants of Stunting in Indonesia: A Systematic Review of Nutritional Interventions and Antenatal Care
Bibliographic record
Abstract
Background: Stunting is a significant global health issue, especially in Indonesia, where long-term malnutrition adversely affects children's growth and development, with prevalence rates still exceeding the WHO's recommended threshold. This study examines the contextual factors that influence the effectiveness of nutritional interventions and antenatal care (ANC) in combating stunting in Indonesia. Methods: A systematic review was conducted by the PRISMA guidelines. A comprehensive search across five major academic databases (Google Scholar, PubMed, ScienceDirect, Embase, and ProQuest) identified 3,690 articles. After a rigorous screening process, 13 studies published between 2019 and 2023 were included in the analysis, focusing on key contextual factors that impact stunting interventions in Indonesia. The quality appraisal utilized Joanna Briggs Institute checklists for analytical cross-sectional studies, cohort studies, quasi-experimental studies, qualitative research, systematic reviews, case reports, and text and opinion papers, each matched to the respective study design. Findings: The review identified four critical contextual factors shaping stunting interventions: (1) socioeconomic status, particularly household income and education, which significantly influence access to healthcare and nutrition; (2) cultural beliefs, including food taboos and misconceptions, which hinder optimal nutritional practices; (3) geographical disparities, with rural populations experiencing higher stunting rates due to limited access to healthcare and resources; and (4) government policies, highlighting the importance of strong political commitment, multisectoral collaboration, and localized programs. Conclusion: Nutritional interventions and ANC are more effective in reducing stunting among Indonesian children when tailored to local socioeconomic, cultural, and geographical contexts. These findings highlight the need for targeted, context-specific strategies to improve child growth outcomes in vulnerable populations.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".