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Record W2893988110 · doi:10.1093/pubmed/fdy168

Determinants of under-5 mortality in Burkina Faso

2018· article· en· W2893988110 on OpenAlexaff
Aristide Romaric Bado, A Sathiya Susuman

Bibliographic record

VenueJournal of Public Health · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDemographySocioeconomic statusInequalityOddsParity (physics)GeographyChild mortalityOdds ratioMultivariate analysisMultivariate statisticsMedicineLogistic regressionPopulationSociologyStatisticsMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this article is to determine the factors associated with under-5 mortality and their evolution from 1993 to 2010 and to analyse the contributors of socioeconomic inequalities in mortality of children under-5 years during the same period. DATA AND METHODS: The data used in this study were derived from the four rounds of Demographic and Health Survey (DHS) conducted in Burkina Faso in 1993, 1998 and 2010. Concentration measurement, logistics regression and Oaxaca-Blinder decomposition method were used to analyse data. RESULTS: Multivariate analysis revealed that being the first child (odds ratio = 1.8 for 1993, 1.7 for 1998, 1.2 for 2003 and 1.3 for 2010) or a twin (odds ratio = 4.5 for 1993, 2.8 for 1998, 2.7 for 2003 and 4.8 for 2010) were also significantly associated with the probability of dying. The variable (parity) was the main contributor to the part of the inequality due to differences in group characteristics and that would be due to the fact that women from poor households have greater parity compared to those from rich households. CONCLUSION: For a reduction in mortality and inequalities related to mortality, the implementation of actions in favour of poor households and promotion of family planning programmes for birth spacing will be required.

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 score0.013
Threshold uncertainty score0.290

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 head0.088
GPT teacher head0.404
Teacher spread0.316 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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