Effective Coverage of Modern Contraceptive Use in Ethiopia: An Ecological Linking Analysis of Service Provision Assessment and National Health Equity Surveys
Bibliographic record
Abstract
The increase in contraceptive prevalence rate (crude coverage) in Ethiopia over the past two decades does not necessarily reflect service quality, and although the proportion of women with unmet needs has decreased, it remains unacceptably high. Hence, this study aimed to estimate the effective coverage (EC) of modern contraceptive methods in Ethiopia, considering the quality of care. We used nationally representative surveys, such as health facility surveys (Ethiopia Service Provision Assessment, 2021/22) and household surveys (National Health Equity Survey, 2022/2023). The descriptive analysis and ecological linking of the two surveys were used to assess the relationship between service quality and utilization among married/in union women in need of limiting or spacing children. In 2022, about 78% of health facilities in Ethiopia were ready to provide Family Planning (FP) services using modern contraceptive methods. Met FP need was 48%, with the quality of services assessed at 36%. After accounting for both service quality and readiness, Ethiopia's effective coverage of family planning services using modern methods was estimated at 16%, with the highest coverage in the Sidama region (21%) and the lowest in the Somali region (2%). The EC of FP services in Ethiopia was low, largely attributed to the poor overall quality of the FP services provided. It is therefore important to ameliorate the quality of FP services in the country.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".