Efektivitas Mobile Health “Nifas Sehat” terhadap Selfcare Agency pada Ibu Nifas Primigravida
Bibliographic record
Abstract
The World Health Organization (WHO) reported that in 2019, there were 303,000 maternal deaths worldwide, with 99% of them occurring in developing countries. According to the Ministry of Health, the maternal mortality rate in Indonesia was 305 per 100,000 live births in 2012-2015, dropping to 240 per 100,000 live births in 2019. However, this figure is still high compared to developed countries which have a maternal mortality rate of 14 per 100,000 live births.The postpartum period is a critical period for mothers, where 60% of maternal deaths occur after delivery and 50% of these deaths occur in the first 24 hours of the postpartum period. Complications during the postpartum period occur in 73% of cases, but not all of them result in maternal death. Primigravida is a term used to describe a woman who is pregnant for the first time. According to WHO data, the maternal mortality rate for primigravida women is higher than for multigravida women. The most common causes of maternal death in Indonesia are bleeding, hypertension during pregnancy, and infection. The Ministry of Health is working to improve the health service system to reduce maternal and infant mortality rates, including during the Covid-19 pandemic. The aim of this study was to measure the level of self-care agency in primigravida postpartum mothers before and after using mobile health "Sehat Postpartum".
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".