Development of an Android-Based Application to Prevent Baby-Born Stunted
Bibliographic record
Abstract
INTRODUCTION: Stunting is still a problem for infants under five years old in Indonesia. One solution to improve the health status of pregnant women to prevent the child born stunting in the digital era is to design an application called ACALS, the “Aplikasi Cegah Anak Lahir Stunting” (ACALS)/ Application for preventing the birth of children with stunting. The application was created as an innovation and strategy for preventing and handling stunting. This study aims to evaluate the design and structure of ACALS as an application used to support the optimization of antenatal care for pregnant women. METHODOLOGY: The research design is Research and Development to evaluate the quality of innovative products in applications with user authority for pregnant women. This research was conducted in 2019 at the Jonggol Community Health Center, Bogor Regency, West Java, Indonesia. Through this research, a smartphone-based application called the ACALS/ Application was designed to prevent the birth of children with stunting, which was developed based on the Ministry of Health's MCH Handbook. RESULT: The application is considered high quality based on its evaluation of all dimensions, which include completeness, correctness, security, timeliness, benefits, efficiency, reliability, and usability. Therefore, the design and structure of ACALS have been significantly developed. CONCLUSION: It was concluded that ACALS is very beneficial for pregnant women during pregnancy as a means of monitoring and supporting the optimization of antenatal care to prevent the birth of stunted babies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".