Prevalence and Determinants of Rural-Urban Utilization of Skilled Delivery Services in Northern Ghana
Bibliographic record
Abstract
There are wide differences in the uptake of skilled delivery services between urban and rural women in the northern region of Ghana. This study assessed the rural-urban differences in the prevalence of and factors associated with uptake of skilled delivery in the northern region of Ghana.The study population comprised postpartum women who had delivered within the last three months prior to the study. The dataset was analyzed using the chi-square test and multivariable logistic regression.The odds of skilled birth attendance (SBA) adjusted for confounding variables in urban areas were higher compared with their rural counterparts (AOR = 1.59; CI: 1. 07-2.37; p=0.02). The determinants of skilled delivery were similar but of different levels and strength in rural and urban areas. The main drivers that explained the relatively high skilled delivery coverage in the urban areas were higher frequency of antenatal care (ANC) attendance, proximity (physical access) to health facility, and greater proportion of women attaining higher educational level of at least secondary school. Distance from health facility less than 4 km was the greatest independent contributor to the variance in skilled delivery in the urban areas, whereas frequency of ANC attendance was the greatest independent contributor in the rural areas.This study identified underlying determinants accounting for rural-urban differences in skilled delivery, and covariate effect was more dominant than coefficient effect. Therefore, urban-rural differences in SBA outcomes were primarily due to differences in the levels of critical determinants rather than the nature of the determinants themselves. Therefore, improving skilled delivery outcomes in this study population and other similar settings will not require different policy frameworks and interventions in dealing with rural-urban disparities in SBA outcomes. However, context-specific tailored approaches and strategies including targeting mechanisms have to be designed differently to reduce the rural-urban differences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".