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Record W2095519644 · doi:10.12927/whp.2007.18853

Inequalities in Reproductive Healthcare Utilization: Evidence from Bangladesh Demographic and Health Survey 2004

2007· article· en· W2095519644 on OpenAlexvenueno aff
Amal Halder, Unnati Rani Saha, Morvarid Kabir

Bibliographic record

VenueWorld health & population · 2007
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedInequalityHealth careReproductive healthOutreachEquity (law)SocioeconomicsEnvironmental healthHealth facilityDeveloping countrySocioeconomic statusEconomic growthMedicinePopulationPolitical scienceEconomicsHealth services

Abstract

fetched live from OpenAlex

Utilization of reproductive healthcare services such as antenatal care (ANC), delivery place facilities and postnatal care (PNC) is essential and a basic need for mothers around the globe. However, in Bangladesh inequalities in many forms affect the use of these facilities. These inequalities include socio-economic status, age, education, household size, existence of living children, occupation and household location. Using the database from the Bangladesh Demographic and Health Survey (BDHS) 2004, this study investigated the inequalities and implications of receiving facility-based maternity care such as ANC, delivery place and PNC in Bangladesh. Based on our findings, it is assumed that with the current inequalities in wealth and education, less attention to mothers with bigger family size and to mothers those existing children, lack of facilities and awareness, in rural areas, increased use of reproductive healthcare is unlikely without a change in wealth inequalities and attention to more equity in the health sector. Bivariate and multivariate analyses were done for the study, including tests of significance. Overall, findings revealed significant socio-economic inequalities in the use of reproductive healthcare services. Use of services was much lower among the poor than the rich. These socio-economic inequalities may be reduced by expanding outreach health programs and bringing services closer to the disadvantaged (poor people). The study concluded that many of these inequalities are social constructs that can be reduced by prioritizing the needs of the poor and disadvantaged and adopting appropriate policy change options.

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)0.004
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 score0.069
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.152
GPT teacher head0.406
Teacher spread0.254 · 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

Citations20
Published2007
Admission routes1
Has abstractyes

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