Why women utilize traditional rather than skilled birth attendants for maternity care in rural Nigeria: Implications for policies and programs
Bibliographic record
Abstract
OBJECTIVE: Data from the Nigeria Demographic and Health Survey indicate that many pregnant women in rural Nigeria use traditional birth attendants (TBAs) rather than skilled birth attendants (SBAs) for maternal health care. This is one factor that accounts for the persistently high rate of maternal mortality in Nigeria. The objective of this study was to identify the pervading reasons that women use TBAs for pregnancy care in rural Nigeria and to make recommendations for policy and programmatic reform. DESIGN: Qualitative research design consisting of focus group discussions, key informant interviews, and community conversations, followed by inductive thematic analysis. SETTING: Twenty rural communities (villages) in Etsako East, and Esan South East Local Government Areas of Edo State, South-South, Nigeria. PARTICIPANTS: Twenty focus group discussions with men and women in a marital union; 15 key informant interviews with policymakers, senior health providers, and women leaders; and 10 community conversations with key community leaders. FINDINGS: Some reasons proffered for using TBAs included perceptions of higher efficacy of traditional medicines; age-long cultural practices; ease of access to TBAs as compared to SBAs; higher costs of services in health facilities; and friendly attitude of TBAs. KEY CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: The continued use of TBA is a major challenge in efforts to achieve the Sustainable Development Goal 3 in Nigeria. We conclude that efforts to address the factors identified by community stakeholders as inhibiting the use of SBAs will promote skilled birth attendance and reduce maternal mortality in rural 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".