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Record W4410521168 · doi:10.1186/s12884-025-07646-5

“Is she pregnant with Jesus?” exploring sociocultural obstacles to following medical advice in the context of stillbirth prevention in Nigeria

2025· article· en· W4410521168 on OpenAlexaff
Uchenna Gwacham-Anisiobi, Adetola Oladimeji, Victoria Yesufu, Jennifer J. Kurinczuk, Manisha Nair, Jenny McLeish

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsWomen's College Hospital
FundersClarendon FundBalliol College, University of OxfordMedical Research CouncilNuffield Department of Population Health, University of Oxford
KeywordsFocus groupMedicineThematic analysisGrassrootsQualitative researchSociocultural evolutionReproductive medicinePsychological interventionReproductive healthContext (archaeology)Family medicineNursingPregnancyPopulationEnvironmental healthSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Each year 182,000 babies are stillborn in Nigeria, representing nearly 10% of the annual global stillbirth burden. Imo state in south-eastern Nigeria has one of the highest levels of maternal health service access in Nigeria, yet this has not translated into good pregnancy outcomes. Many stillbirth prevention initiatives in Nigeria focus on maternal health education but empirical evidence suggests that sociocultural factors impact healthcare choices and outcomes. This study aims to explore women's and health workers' perspectives of the sociocultural barriers to following medical advice during pregnancy and childbirth, and specifically how these barriers may contribute to an increased risk of stillbirth. This study is part of a broader community-based stillbirth prevention mixed-methods research in Imo State, Nigeria. METHODS: A qualitative descriptive study was conducted using in-depth interviews and focus group discussions. 38 participants were purposively recruited; 20 women and 18 health workers. Audio recordings were transcribed, translated and analysed using inductive thematic analysis. RESULTS: Four themes were identified: (1) trust, where scepticism about health worker motives or competence and trust in community informal networks were highlighted (2) power dynamics within families, with husbands and older female relatives influencing health decisions; (3) personal and community beliefs that undermine confidence in medical interventions, including a pervasive stigma associated with caesarean section; and (4) grassroots proposals for solutions, emphasising the importance of a whole-community approach to maternal health education, mobilising peer voices, engaging traditional leaders and training of traditional birth attendants. CONCLUSION: This study provides insights into the sociocultural barriers to following medical advice during pregnancy in Nigeria, which include a lack of trust in health professionals, power dynamics within a woman's family, and entrenched cultural and religious beliefs that oppose medical intervention. Women's decisions about pregnancy and childbirth are heavily influenced by family and cultural norms. Culturally sensitive, community-wide interventions which aim to rebuild trust in the health system, involve women as decision-makers in antenatal care, and engage religious and traditional leaders would be beneficial for improving outcomes.

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)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.298
Teacher spread0.273 · 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 designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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