Spatial Analysis of Skilled Birth Attendant Utilization in Ghana
Bibliographic record
Abstract
BACKGROUND: Maternal mortality is a major health problem in most resource-poor settings, especially in sub-Saharan Africa. In Ghana, maternal mortality remains high and births attended by skilled health professionals are still low despite the introduction, in 2005, of free maternal health care for all women seeking care in public health facilities. OBJECTIVES: This study aimed to explore geographical patterns in the risk of not utilizing a skilled birth attendant during childbirth in women of different socioeconomic backgrounds in Ghana. METHODS: Global and Geographically Weighted Odds Ratios (GWORs) were used to examine the spatially varying relationships between low socioeconomic status (low education and low income) and non-utilization of skilled birth attendants based on data from the Ghana Demographic and Health Survey (GDHS) 2008. RESULTS: Low education and low income were associated with non-use of skilled birth attendants. The GWORs revealed a north-south spatial variation in the magnitude of the association between non-use of skilled birth attendants and low education (Log GWOR ranged from 0.75 to 9.26) or low income (Log GWOR ranged from 1.11 to 6.34) with higher values in the north. CONCLUSIONS: The relationship between low socioeconomic status and the non-use of skilled birth attendants in Ghana is geographically variable. Effective governmental and non-governmental interventions are needed to address these regional inequalities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".