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Record W2149446222 · doi:10.1016/s2214-109x(14)70008-7

Socioeconomic inequality in neonatal mortality in countries of low and middle income: a multicountry analysis

2014· article· en· W2149446222 on OpenAlex
Britt McKinnon, Sam Harper, Jay S. Kaufman, Yves Bergevin

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".

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Lancet Global Health · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsInequalitySocioeconomic statusDisadvantagedIndex (typography)Survey data collectionDistribution (mathematics)DemographyDemographic economicsGeographyEconomicsPopulationEconomic growthSociologyMathematicsStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Neonatal mortality rates (NMRs) in countries of low and middle income have been only slowly decreasing; coverage of essential maternal and newborn health services needs to increase, particularly for disadvantaged populations. Our aim was to produce comparable estimates of changes in socioeconomic inequalities in NMR in the past two decades across these countries. METHODS: We used data from Demographic and Health Surveys (DHS) for countries in which a survey was done in 2008 or later and one about 10 years previously. We measured absolute inequalities with the slope index of inequality and relative inequalities with the relative index of inequality. We used an asset-based wealth index and maternal education as measures of socioeconomic position and summarised inequality estimates for all included countries with random-effects meta-analysis. FINDINGS: 24 low-income and middle-income countries were eligible for inclusion. In most countries, absolute and relative wealth-related and educational inequalities in NMR decreased between survey 1 and survey 2. In five countries (Cameroon, Nigeria, Malawi, Mozambique, and Uganda), the difference in NMR between the top and bottom of the wealth distribution was reduced by more than two neonatal deaths per 1000 livebirths per year. By contrast, wealth-related inequality increased by more than 1·5 neonatal deaths per 1000 livebirths per year in Ethiopia and Cambodia. Patterns of change in absolute and relative educational inequalities in NMR were similar to those of wealth-related NMR inequalities, although the size of educational inequalities tended to be slightly larger. INTERPRETATION: Socioeconomic inequality in NMR seems to have decreased in the past two decades in most countries of low and middle income. However, a substantial survival advantage remains for babies born into wealthier households with a high educational level, which should be considered in global efforts to further reduce NMR. FUNDING: Canadian Institutes of Health Research.

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)0.002
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 score0.003
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.022
GPT teacher head0.343
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