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Record W2124142618 · doi:10.5539/gjhs.v5n3p34

Performance Based Financing and Uptake of Maternal and Child Health Services in Yobe Sate, Northern Nigeria

2013· article· en· W2124142618 on OpenAlexvenueno aff
Garba Mohammed Ashir, Henry V. Doctor, Godwin Y. Afenyadu

Bibliographic record

VenueGlobal Journal of Health Science · 2013
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsProxy (statistics)Context (archaeology)MedicineMeaslesChild mortalityEnvironmental healthChild survivalMillennium Development GoalsDeveloping countrySocioeconomicsEconomic growthBusinessDemographyGeographyPopulationEconomics

Abstract

fetched live from OpenAlex

Reported maternal and child health (MCH) outcomes in Nigeria are amongst the worst in the world, with Nigeria second only to India in the number of maternal deaths. At the national level, maternal mortality ratios (MMRs) are estimated at 630 deaths per 100,000 live births (LBs) but vary from as low as 370 deaths per 100,000 LBs in the southern states to over 1,000 deaths per 100,000 LBs in the northern states. We report findings from a performance based financing (PBF) pilot study in Yobe State, northern Nigeria aimed at improving MCH outcomes as part of efforts to find strategies aimed at accelerating attainment of Millennium Development Goals for MCH. Results show that the demand-side PBF led to increased utilization of key MCH services (antenatal care and skilled delivery) but had no significant effect on completion of child immunization using measles as a proxy indicator. We discuss these results within the context of PBF schemes and the need for a careful consideration of all the critical processes and risks associated with demand-side PBF schemes in improving MCH outcomes in the study area and similar 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)0.001
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 score0.044
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.268
Teacher spread0.260 · 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

Citations45
Published2013
Admission routes1
Has abstractyes

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