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Record W2567656882 · doi:10.29063/ajrh2016/v20i3.20

Reducing Maternal Mortality by Strengthening Community Maternal Support Systems: Findings from a Qualitative Baseline Study in Northern Nigeria

2016· article· en· W2567656882 on OpenAlexaboutno aff
Ekechi Okereke, Susan Aradeon, Ibrahim Yisa

Bibliographic record

VenueAfrican Journal of Reproductive Health · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersDepartment for International Development
KeywordsMedicineCommunity health workersMaternal morbidityDeveloping countryEnvironmental healthRural communityMaternal healthMaternal deathQuarter (Canadian coin)Rural areaCommunity healthPopulationPregnancyEconomic growthPublic healthSocioeconomicsNursingGeographyHealth servicesSociology

Abstract

fetched live from OpenAlex

The "three delays model" illustrates how issues around obstetric emergency can lead to maternal deaths. This study applied in-depth interviews of key community gatekeepers in 16 rural communities across two states in northern Nigeria to evaluate the presence and functionality of key community maternal support systems for reducing maternal mortality. Findings show that only one out of the 16 communities had all the key support systems. Five rural communities reported that pregnant women have standing permission to visit health facilities during obstetric emergencies. A quarter of the communities reported the presence of transport for maternal emergencies. One rural community each reported the existence of community savings for obstetric emergencies and the presence of blood donor groups. Establishing and/or strengthening community support systems, ensuring citizens are well-informed about maternal danger signs and preparing for safe pregnancies can enable communities overcome the delays and reduce maternal mortality especially in low resource settings.

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)0.009
metaresearch head score (Gemma)0.001
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 score0.043
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
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 science0.0000.000
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 head0.045
GPT teacher head0.365
Teacher spread0.320 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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