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Record W4406467727 · doi:10.1101/2025.01.08.632060

Women at the receiving end: Exploring Couples’ experiences of infertility challenges in Nigeria

2025· preprint· en· W4406467727 on OpenAlexaff
Deborah Tolulope Esan, Kelechukwu Queendaline Nnamani, Adetunmise Oluseyi Olajide, Oluwadamilare Akingbade, David Bamidele Oluwade, David B. Olawade, Carlos Ramos

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInfertilityMedicineObstetricsGynecologyPregnancyBiology

Abstract

fetched live from OpenAlex

Abstract Background Infertility remains a significant global health issue with profound emotional and social consequences, particularly in patriarchal societies. This study explores the lived experiences of infertile couples in Nigeria, focusing on societal perceptions, gendered blame, and the role of spirituality. Methods A qualitative exploratory design was adopted, with data collected from purposively sampled infertile couples attending the Federal Teaching Hospital, Ido-Ekiti, Nigeria. Semi-structured interviews were conducted, and thematic analysis was used to identify recurring patterns and themes. Results Thematic analysis identified two main themes and seven sub-themes. The first theme, Couples ’ Perception of Infertility, included five sub-themes: definition of infertility, perceived causes, perceived types, infertility as a feminine issue, and the role of spirituality. The second theme, Challenges of Couples Experiencing Infertility, comprised two sub-themes: women at the receiving end and poor societal support. Conclusion The findings shows the interplay of medical, cultural, and spiritual dimensions in shaping infertility perceptions. The study highlights the disproportionate burden on women and inadequate societal support, calling for public health interventions to address stigma and promote gender-inclusive reproductive health education.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0010.001
Research integrity0.0000.001
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 head0.075
GPT teacher head0.282
Teacher spread0.207 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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