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Record W2945230975 · doi:10.1186/s12889-019-6945-4

Do four or more antenatal care visits increase skilled birth attendant use and institutional delivery in Bangladesh? A propensity-score matched analysis

2019· article· en· W2945230975 on OpenAlexaff
Bridget Ryan, Rohin J. Krishnan, Amanda Terry, Amardeep Thind

Bibliographic record

VenueBMC Public Health · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicinePropensity score matchingBiostatisticsBirth attendantDeveloping countryPrenatal carePublic healthHealth careEnvironmental healthPopulationMaternal healthNursingEconomic growthHealth services

Abstract

fetched live from OpenAlex

BACKGROUND: With Bangladesh's adoption of the third Sustainable Development Goal to reduce maternal mortality, the impetus for Bangladesh to continue to improve uptake of maternal healthcare is strong. METHODS: Using a propensity-score matched analysis, the present study utilized data from the 2014 Bangladesh Demographic Health survey to examine the impact of four or more antenatal care visits on skilled birth attendant use and institutional delivery. RESULTS: The results revealed a significant and positive impact of four or more antenatal care visits on skilled birth attendant use and institutional delivery after matching treated and untreated mothers on included socio-demographic characteristics. CONCLUSIONS: Implementation of policies to provide at least four antenatal care visits may serve as an effective strategy to increase SBA use and institutional delivery in Bangladesh, which could contribute to the reduction of maternal mortality.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.012
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.051
GPT teacher head0.294
Teacher spread0.242 · 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

Citations41
Published2019
Admission routes1
Has abstractyes

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