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Strengthened family planning is critical to help accelerate the reduction of maternal mortality in Indonesia

2021· preprint· en· W3198657261 on OpenAlexfundno aff
Budi Utomo, Nohan Arum Romadlona

Bibliographic record

VenueF1000Research · 2021
Typepreprint
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersGlobal Affairs CanadaUniversitas IndonesiaUnited Nations Population Fund
KeywordsChildbirthMedicineFamily planningStandardized mortality ratioMaternal healthPregnancyEnvironmental healthNursingPopulationHealth servicesBiology

Abstract

fetched live from OpenAlex

<ns3:p>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.</ns3:p>

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.123
GPT teacher head0.444
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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