Exploring underutilization of skilled maternal healthcare in rural Edo, Nigeria: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: Existing studies have acknowledged the underutilization of skilled maternal healthcare services among women in rural Nigeria. Consequently, women in rural areas face a disproportionate risk of poor health outcomes including maternal morbidity and mortality. Addressing the challenge of non-use of skilled maternal healthcare in rural areas necessitates the involvement of multi-stakeholders across different sectors who have vital roles to play in improving maternal health. This study explores the factors contributing to the non-use of maternal healthcare services in rural areas of Edo, Nigeria from the perspectives of community elders and policymakers. METHODS: In this qualitative study, data were collected through 10 community conversations (group discussions) with community elders each consisting of 12 to 21 participants, and six key informant interviews with policymakers in rural areas of Edo State, Nigeria. Participants were purposefully selected. Conversations and interviews occurred in English, Pidgin English and the local language; lasted for an average of 9 minutes; were audio-recorded and transcribed to English. Data were manually coded, and data analysis followed the analytical strategies for qualitative description including an iterative process of inductive and deductive approaches. RESULTS: Policymakers and community elders attributed the non-use of maternal health services to poor quality of care. Notions of poor quality of care included shortages in skilled healthcare workers, apathy and abusive behaviours from healthcare providers, lack of life-saving equipment, and lack of safe skilled pregnancy care. Non-use was also attributed to women's complex utilization patterns which involved a combination of different types of healthcare services, including traditional care. Participants also identified affordability and accessibility factors as deterrents to women's use of skilled maternal healthcare. CONCLUSION: The emerging findings on pregnant women's combined use of different types of care highlight the need to improve the quality, availability, accessibility, and affordability of skilled maternal care for rural women in Nigeria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".