The Effectiveness of the SOBUMIL mHealth App in Enhancing Early Detection of Pregnancy Complications in Bogor Regency, Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Global and national efforts are underway to reduce maternal mortality. Empowering pregnant women enables health decision-making and early detection of pregnancy complications. Developing applications related to pregnancy potentially improves women's behavior in preventing pregnancy complications. Objective: This study aimed to explore the influence of SOBUMIL (Sobat Ibu Hamil), an android-based application on pregnant women's empowerment for early detection of complications. Methods: A quasi-experimental study was conducted in the Bogor Regency, Indonesia. Study participants were pregnant women residing in two primary health care in their second and third trimesters. Pregnant women were excluded if they were disabled or unable to read and write. A total sample of 350 was calculated using the Lemeshow sample formula, which included an intervention and control group. Results: Overall, we found a statistically significant positive effect of SOBUMIL application in all pregnant women's empowerment parameters to detect pregnancy complications early in Bogor Regency (p<0.001). Conclusion: This study confirms the positive influence of the SOBUMIL application in empowering pregnant women for early detection of pregnancy complications. This underscores the potential of mobile health interventions to enhance knowledge, attitudes, and abilities, enabling independent monitoring and addressing of pregnancy-related risks, ultimately improving maternal healthcare outcomes.
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.
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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it