Environmental Health Determinants of Stunting Incidence: An Observational Study in Densely Populated Settlements
Bibliographic record
Abstract
Background: Stunting, a form of chronic malnutrition manifesting in early childhood, typically becomes apparent by the age of two. It inhibits physical growth, disrupts brain development, and increases the risk of disease in children, thus affecting the quality of future human resources. Babakan Penghulu sub-district, with 53 cases of Stunting in 2023, has been designated a priority area for stunting control program by the Cinambo Health Center. As a densely populated and flood-prone area in Bandung, Babakan Penghulu suffers from inadequate environmental sanitation, which adversely impacts toddler health and development. Objective: This study aims to identify environmental factors related to the incidence of stunting in the Babakan Penghulu sub-district. This study employs an analytical observational design with a cross-sectional approach. Method: This research is a quantitative research. The population comprised 458 mothers with toddlers. A sample of 79 mothers was selected using proportional random sampling across 8 neighborhood units. Data were collected through observation sheets, and data analysis was conducted using the Chi-Square Test. Results: The results showed a relationship between drinking water quality (P-value:0.018), waste disposal facilities (P-value:0.044), and access to healthy toilet facilities (P-value:0.001). In contrast, household wastewater disposal facilities (P-value:0.089) not show a significant relationship. Conclusion: Stunting in toddlers is significantly associated with environmental sanitation factors such as the quality of drinking water, waste disposal facilities, and toilet hygiene. However, household wastewater disposal wasn’t significantly related. Strengthening community-based total sanitation infrastructure and promoting community health initiatives, with support and supervision from the local health office and the Bandung City government, are recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".