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Gender, pregnancy and the uptake of antenatal care services in Pakistan

2007· article· en· W1745345587 on OpenAlexaff
Zubia Mumtaz, Sarah Salway

Bibliographic record

VenueSociology of Health & Illness · 2007
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPregnancyObstetricsPrenatal careMedicineEnvironmental healthPopulationBiology

Abstract

fetched live from OpenAlex

An integrated analysis of detailed ethnography and large-scale survey data is presented to explore the gendered influences on women's uptake of antenatal care (ANC) services in Punjab, Pakistan. Pregnancy and its associated decisions were shown to be normatively the older women's domain, with pregnant women and their husbands being distanced from the decision-making process. Women who successfully claimed ANC did so not by overtly challenging the dominant construction of young femininity, but rather by using existing gendered structures and channels of communication to influence authority figures. The quality of a woman's inter-personal ties, particularly with her mother-in-law and husband, were found to be important in accessing resources, including ANC. Gendered influences were moderated by social class. Family finances were an important determinant of ANC use, as was women's education. Wealthier, higher status women also found it easier to circumvent gendered proscriptions against their mobility while pregnant. As well as illuminating the ways in which the sociocultural construction of gender acts to constrain women's access to ANC, the empirical findings are used to highlight significant inadequacies in the 'autonomy paradigm' that has dominated much of the research into women's reproductive health in South Asia.

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)0.003
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 score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
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 head0.016
GPT teacher head0.344
Teacher spread0.328 · 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

Citations196
Published2007
Admission routes1
Has abstractyes

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