Socioeconomic and Environmental Determinants of Community Stunting Prevention Behaviors: A Systematic Literature Review
Bibliographic record
Abstract
Stunting represents a critical public health challenge in developing countries, affecting over a quarter of children globally and resulting from complex interactions between socioeconomic, environmental, and behavioral factors that require comprehensive understanding for effective intervention strategies. This study aimed to identify, analyze, and synthesize the determinants of stunting prevention behaviors in developing countries through systematic literature review to provide evidence-based recommendations for targeted intervention strategies. A systematic literature review following PRISMA 2020 guidelines was conducted using PubMed, Scopus, Science Direct, and Google Scholar databases. Search terms included "stunting," "malnutrition," "determinant factors," "prevention," "community behavior," and "developing countries." Inclusion criteria encompassed articles published 2019-2024 focusing on children aged 0-59 months in developing countries. Quality assessment utilized Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool, resulting in 20 high-quality studies from 309 initially identified articles. Maternal education emerged as the most consistent determinant, with low educational levels significantly correlated with suboptimal parenting practices. Economic status demonstrated complex relationships where poverty limited nutritious food access and affected family resource allocation priorities. Health promotion models proved effective for behavioral change, with self-efficacy and social support as significant factors. Environmental and sanitation factors played crucial roles through infection prevention and optimal nutrient absorption mechanisms. Multisectoral collaborative approaches emerged as most effective strategies requiring stakeholder trust and integrated coordination mechanisms. Comprehensive stunting prevention requires holistic intervention programs integrating maternal education, economic empowerment, and improved healthcare access. Strategic recommendations include participatory health education approaches, community-involved sanitation infrastructure development, and multisectoral collaboration models with neutral coordination platforms and shared performance indicators for sustainable program implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".