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Record W3086467954 · doi:10.1093/fampra/cmaa053

Factors affecting the utilization of antenatal care services among women in Guinea: a population-based study

2020· article· en· W3086467954 on OpenAlexaff
Gebretsadik Shibre, Betregiorgis Zegeye, Dina Idriss-Wheeler, Sanni Yaya

Bibliographic record

VenueFamily Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsGlobal Affairs CanadaUniversity of Ottawa
Fundersnot available
KeywordsMedicineLogistic regressionResidenceOdds ratioConfidence intervalDemographyEthnic groupPopulationEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Evidence suggests that a better understanding of determinants of antenatal care (ANC) utilization is crucial to reducing maternal and child deaths. Little is known about the utilization of ANC services in Guinea. OBJECTIVE: The aim of this study was to explore factors determining utilization of skilled ANC in Guinea. METHODS: This study focused on a sample of 7812 ever married women. Using multivariate logistic regression, factors associated with the utilization of ANC were identified. The output of the multivariate logistic regression was presented using adjusted odds ratio and the corresponding 95% confidence interval (CI). RESULTS: Several factors had significant association with utilization of skilled ANC service in Guinea: having decision-making power (2.21, 95% CI: 1.63, 3.00), employment status (1.86, 95% CI: 1.39, 2.48), media exposure (1.60, 95% CI: 1.26, 2.02), maternal education (2.68, 95% CI: 1.36, 5.28), husband/partner education status (1.66, 95% CI: 1.08, 2.55), household economic status (2.19, 95% CI: 1.40, 3.44), place of residence (0.29, 95% CI: 0.16, 0.50) and ethnicity (0.40, 95% CI: 0.23, 0.69). CONCLUSIONS: These findings suggest a variety of socio-demographic and economic factors as well as media exposure are associated with women's use of ANC. Policymakers should implement appropriate measures to address the existing variations and gaps in ANC services utilization among different subgroups of women in Guinea.

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 score0.138
Threshold uncertainty score0.797

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 head0.046
GPT teacher head0.330
Teacher spread0.284 · 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

Citations37
Published2020
Admission routes1
Has abstractyes

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