Equitable Distribution of Poor Quality of Care? Equity in Quality of Reproductive Health Services in Ethiopia
Bibliographic record
Abstract
The Ethiopian health system faces persistent inequities in health-care utilization and outcomes, despite continued efforts to expand health service coverage. There is little evidence in the literature describing the status of equity in the quality of healthcare. This paper aims to understand the disparities in quality of antenatal care (ANC) and family planning (FP) among the poor and non-poor communities. We used the 2016 Ethiopia Demographic and Health Survey (DHS) data to compute a Multidimensional Poverty Index (MPI), and the 2014 Service Provision Assessment (SPA) data to assess quality of ANC and FP services-defined as the level of adherence to World Health Organization (WHO) clinical and service guidelines. We merged the two datasets using geographical coordinates, and aggregated service users into facility catchment area clusters using a 2-km radius for urban and 10-km radius for rural facilities. We computed ANC and FP quality and MPI indices for each facility and assigned these to catchment areas. Using the international cutoff point for deprivation (MPI = 33.3%), we evaluated whether the quality of ANC and FP services varies by poor and non-poor catchment areas. We found that most of catchment areas (75.7%) were deprived. While the overall quality of ANC and FP services are low (33% and 34% respectively), we found little variation in the distribution of the quality of these services between poor and non-poor areas, urban and rural settings, or regionally. The short-term focus needs to be on improving the overall quality of services rather than on its distribution.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".