Factors associated with maternal utilization of health facilities for delivery in Ethiopia
Bibliographic record
Abstract
Background: As a signatory of the Millennium Development Goals, Ethiopia has made significant achievements towards meeting the maternal mortality related goals since 1990. Despite that, the country is still experiencing unacceptably high maternal mortality rates, and challenges to improving the coverage and utilisation of health facility delivery services which represent a key strategy to combat maternal mortality. Currently, there is limited evidence on the factors associated with health facility delivery in Ethiopia. Therefore, the objective of this study was to identify the correlates of facility delivery among urban and rural women in Ethiopia. Methods: This was a cross-sectional study based on data from the 2011 Ethiopian Demographic and Health Survey. Participants were 7540 women aged between 15 and 49 years with a history of at least one birth. The outcome variable was place of delivery. Data were analyzed using bivariate and multivariable regression techniques. Results: The overall prevalence of health facility delivery was 17.1% (1447/7540). In the multivariable regression analysis, education, wealth status, frequency of antenatal care visits and mother's age at first birth were found to be significantly associated with women's choice of place of delivery. Among urban women, those who had primary and secondary/higher level education had increased odds of delivering at a health facility compared with those without formal education. Those who were from the richest households had higher odds of delivering at a health facility compared with those in the lowest class. In urban and rural areas, compared with those who had no ANC visits, those who had at least four visits also had increased odds of delivering at a health facility. In the urban areas, those who were over 18 years old at their first childbirth had significantly higher odds of choosing to deliver at a health facility. Conclusion: Findings show that the prevalence of healthy facility delivery in Ethiopia is remarkably low. Addressing the sociodemographic and wealth inequities can help promote the utilisation of facility delivery in both urban and rural areas. Policy-makers should consider improving access to education as a strategy to meet maternal health related goals and treat education as a multipronged strategy. Providing free healthcare access could be one strategy to achieve the universal coverage of essential maternal healthcare services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".