HUBUNGAN STIKERISASI PROGRAM PERENCANAAN PERSALINAN DAN PENCEGAHAN KOMPLIKASI DENGAN PENANGANAN KOMPLIKASI
Bibliographic record
Abstract
The direct causes of maternal mortality in Indonesia is bleeding (32%), postpartum hemorrhage, eclampsia (13%), unsafe abortion (11%), infection (10%) and obstructed labor (9%). P4K with stickers is a breakthrough in reducing maternal mortality and newborn. The high incidence of obstetric complications in the District of Talbot District Seluma. The purpose of the study to determine the relationship stikerisasi P4K with handling complications in postpartum mothers. The study design was a descriptive, cross-sectional design. Data obtained from secondary data sheet checklist. The samples were puerperal women who experience complications. Sampling with a total sampling technique. The results showed 81.4% had 87.3% stikerisasi P4K and already getting treatment complications. The results showed no relationship between stikerisasi P4K with handling complications (p = 0,000 < ,005 and OR = 55,688). Expected midwife as further improve performance by IEC on P4K.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".