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Record W2883172324

SHORT REPORT: Demographic and Sociocultural Factors Influencing Use of Maternal Health Services in Ghana

2017· article· en· W2883172324 on OpenAlexaff
Isaac Addai

Bibliographic record

VenueAfrican Journal of Reproductive Health · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsAcadia University
Fundersnot available
KeywordsResidenceLogistic regressionSociocultural evolutionEthnic groupPopulationDeveloping countryPrenatal careMedicineEnvironmental healthDemographyGeographyPsychologyGerontologyEconomic growthSociology
DOInot available

Abstract

fetched live from OpenAlex

Using data from the 1993 Ghana Demographic and Health Survey (GDHS), this study investigates the demographic and sociocultural determinants of use of maternal health services. The maternal health services considered in this study are: i) use of a doctor for prenatal care; ii) soliciting antenatal check-up; iii) place of delivery and, iv) family planning. Logistic regression is employed to explore the relative importance of age at marriage, number of living children, education, place of residence, occupation, region of residence, religion, ethnicity, and age on the likelihood of using maternal health services. Multivariate analyses reveal that the use of the four maternal health services under study tend to be shaped mostly by level of education, place of residence, region of residence, occupation, and religion. Programmatic implications of these results are discussed. (AfrJReprod Health 1998;2(l):73—80) KEY WORDS: Demographic, sociocultural, maternal health, use of services

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)0.000
metaresearch head score (Gemma)0.004
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.324
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 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
Published2017
Admission routes1
Has abstractyes

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