Multifactorial Determinants of Stunting among Under-Five Children in Tambun Tulang Village, South Coastal District, Indonesia
Bibliographic record
Abstract
Background: Stunting is a chronic condition of impaired growth in children under five, primarily due to long-term malnutrition. It is identified using the height-for-age (TB/U) indicator, where a Z-score below -2 standard deviations (SD) from the WHO child growth standards signifies stunting. In Kenagarian IV Koto Hilie, 53 children (8.32%) were recorded as stunted. Although malnutrition can begin during pregnancy or shortly after birth, it often becomes evident when the child reaches two years of age. Objective: This study aimed to identify the factors influencing the incidence of stunting among toddlers in Kampung Bukit Tambun Tulang. Methods: A quantitative, cross-sectional study design was employed. The population consisted of 173 toddlers, with a sample of 63 selected through simple random sampling. Primary data were collected via structured interviews using questionnaires, and secondary data were sourced from Kenagarian IV Koto Hilie health reports. Data analysis included univariate and bivariate methods, with the chi-square test employed to identify associations. Results: The study found that 57.1% of the sampled toddlers were stunted. Bivariate analysis revealed significant relationships between stunting and maternal knowledge (p = 0.003), family income (p = 0.022), and nutritious food intake (p = 0.016). Conclusion: Stunting is closely linked to maternal education, socioeconomic conditions, and child nutrition. Health workers should provide targeted education to promote behavioral change and improve parenting practices. Structured family coaching is recommended to support nutritional fulfillment and prevent stunting during early childhood development.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".