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Record W2949049833 · doi:10.17645/pag.v7i2.1835

The Impact of Foreign Aid on Maternal Mortality

2019· article· en· W2949049833 on OpenAlexafffund
Emmanuel Banchani, Liam Swiss

Bibliographic record

VenuePolitics and Governance · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMaternal healthMillennium Development GoalsEconomic growthSustainable developmentEnvironmental healthGlobal healthHealth promotionReproductive healthChild mortalityLow and middle income countriesPolitical sciencePromotion (chess)MedicineDeveloping countryBusinessHealth carePopulationHealth servicesEconomics

Abstract

fetched live from OpenAlex

In 2010, the G8 placed renewed focus on maternal health via the Muskoka Initiative by committing to spend an additional $5 billion on maternal, newborn, and child health before 2015. Following the end of the Millennium Development Goals and the advent of the Sustainable Development Goals, maternal health issues have continued to feature prominently on the global health agenda. Despite these substantial investments of foreign aid over the past decade, there is limited evidence on the effectiveness of foreign aid in reducing maternal mortality in low- and middle-income countries (LMICs). Using data from the Organisation for Economic Cooperation and Development, the World Development Indicators and the Institute of Health Metrics and Evaluation, this study analyzes the effects of aid on maternal health in a sample of 130 LMICs from 1996 through 2015. Our results show that the effects of total foreign aid on maternal mortality are limited, but that aid allocated to the reproductive health sector and directly at maternal health is associated with significant reductions in maternal mortality. Given these targeted effects, it is important to channel more donor assistance to the promotion of reproductive health and contraceptive use among women as it serves as a tool towards the reduction of maternal mortality.

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)0.000
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 score0.207
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.304
Teacher spread0.291 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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