Referee report. For: Strengthened family planning is critical to help accelerate the reduction of maternal mortality in Indonesia [version 1; peer review: 1 approved]
Bibliographic record
Abstract
The still stubbornly high maternal mortality ratio challenges Indonesia to improve health program strategies to achieve the Sustainable Development Goal 3.1 target of a maternal mortality ratio below 70 per 100,000 live births by 2030. Indonesia has already adopted maternal-neonatal health experts’ recommendation of four core program strategies to reduce maternal mortality: (1) family planning with related reproductive health services; (2) skilled care during pregnancy and childbirth; (3) timely emergency obstetric care; and (4) immediate postnatal care (WHO, 1996). These four core strategies would reduce maternal mortality through reduced high-risk births. To be effective, however, these four core program strategies require continued strong quality assurance and central and local government support to ensure program effectiveness yielded towards widely accessible, sustained, quality family planning and maternal and neonatal emergency services. This paper provides evidence for the importance of family planning to help health program strategies to accelerate maternal mortality reduction.
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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 | 0.006 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.217 | 0.097 |
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".