Stunting Super App as an Effort Toward Stunting Management in Indonesia: Delphi and Pilot Study
Bibliographic record
Abstract
Background: Currently, 30 million children are experiencing acute malnutrition, and 8 million children are severely underweight. Objective: This study aimed to develop a stunting super app, a one-stop app designed to prevent and manage stunting in Indonesia. Methods: This study consisted of three stages. Stage 1 used a 3-round Delphi study involving 12 experts. In stage 2, 4 experts and a parent of children with stunted growth created an Android app containing stunting educational materials. In stage 3, a pilot study involving a control group was conducted to evaluate parents' knowledge about stunting prevention through the app and standard interventions. Results: In the Delphi study, 11 consensus statements were extracted; arranged in three major themes, including maternal health education, child health education, and environmental education; and applied in the form of the Sistem Evaluasi Kesehatan Anak Tumbuh Ideal (SEHATI) app. This app was assessed using a content validity index, with a cumulative agreement of ≥80% among the 5 individuals. The pilot study showed an increase in the knowledge of mothers of toddlers with stunted growth before and after the educational intervention (P=.001). Conclusions: The SEHATI app provides educational content on stunting prevention that can increase the knowledge of mothers of toddlers with stunted growth.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".