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The effect of Kenya’s free maternal health care policy on the utilization of skilled delivery services and maternal and neonatal mortality rates in public health facilities

2017· article· en· W2768688172 on OpenAlexfundno aff
C. M. Gitobu, Peter Gichangi, Walter Mwanda

Bibliographic record

VenueInternational Journal of Community Medicine and Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersAfrican Population and Health Research CenterInternational Development Research Centre
KeywordsPublic healthMedicineKenyaEnvironmental healthService delivery frameworkNeonatal mortalityInfant mortalityPregnancyService (business)BusinessNursingPopulationPolitical science

Abstract

fetched live from OpenAlex

Background: Kenya abolished delivery fees in all public health facilities through a presidential directive effective on June 1, 2013 with an aim of promoting skilled delivery service utilization and reducing pregnancy-related mortality in the country. This paper aims to provide a brief overview of the free maternal health care policy’s effect on skilled delivery service utilization and maternal and neonatal mortality rates in Kenyan public health facilities. Methods: Interrupted time series analysis of skilled delivery services utilization, maternal and neonatal mortality rates two years before and after the policy intervention was carried out in 77 Kenyan public health facilities. Results: A statistically significant increase in the number of facility-based deliveries was identified with no significant changes in the rates of maternal mortality and neonatal mortality. Conclusions: The findings suggest that cost is a deterrent to skilled delivery service utilization in Kenya and thus free delivery services are an important strategy in the effort to promote the utilization of skilled delivery services; however, there is a need to simultaneously address other factors that contribute to pregnancy-related deaths when addressing maternal and neonatal mortality rates.

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)0.006
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 score0.182
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.072
GPT teacher head0.394
Teacher spread0.322 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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