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Record W2532899293 · doi:10.11564/30-3-923

Timing and Frequency of Antenatal Care Utilization in Slums: Assessing Determinants over time

2016· article· en· W2532899293 on OpenAlexaff
Kanyiva Muindi, Blessing Mberu, Patricia Elungata, Maharouf Oyolola

Bibliographic record

VenueAfrican Population Studies · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsEducational attainmentResidenceAttendanceLogistic regressionMultinomial logistic regressionParity (physics)DemographyMedicineEnvironmental healthEthnic groupEconomicsEconomic growthSociologyStatistics

Abstract

fetched live from OpenAlex

Timely and adequate antenatal care (ANC) attendance is important in maternal health. This paper examined the factors associated with ANC utilization in Nairobi slums in 2000 and 2012. Data come from two cross sectional surveys in the slums in Nairobi city. We fitted multinomial and logistic regression models to assess respectively, factors associated with timing of the first ANC visit and the frequency of ANC visits. In both years, parity, mother’s ethnic group and educational attainment were significantly associated with timing of first ANC visit. Frequency of visits was significantly associated with mother’s educational attainment, parity, pregnancy wantedness and place of residence. We conclude that for optimal ANC utilization, there is need to improve women’s educational outcomes and address cultural barriers to utilization

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.375
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

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