Equity dimensions of the availability and quality of reproductive, maternal and neonatal health services in Zambia
Bibliographic record
Abstract
OBJECTIVE: To assess how quality and availability of reproductive, maternal, neonatal (RMNH) services vary by district wealth and urban/rural status in Zambia. METHODS: We conducted a retrospective analysis of data from the Millennium Development Goal Acceleration Initiative baseline assessment of 117 health facilities in 9 districts. Quality was assessed through a composite score of 23 individual RMNH indicators, ranging from 0 to 1. Availability was evaluated by density of providers and facilities. Districts were divided into wealth groups based on the multidimensional poverty index (MPI). Relative inequity was calculated using the concentration index for quality indicators (positive favours rich, negative favours poor). Multivariable linear regression was performed for the dependent variable composite quality indicator using MPI, urban/rural, and facility level of care as independent variables. RESULTS: 13 hospitals, 85 health centres and 19 health posts were included. The RMNH composite quality indicator was 0.64. Availability of facilities and providers was universally low. The concentration index for the composite quality indicator was -0.015 [-0.043, 0.013], suggesting no clustering to favour either rich or poor districts. Rich districts had the highest absolute numbers of health facilities and providers, but lowest numbers per facility per 1 000 000 population. Urban districts had slightly better service quality, but not availability. Using regression analysis, only facility level of care was significantly associated with quality outcome. CONCLUSIONS: Composite quality of RMNH services did not vary by district wealth, but was slightly higher in urban districts. The availability data suggest that the higher population in richer districts outpaces health infrastructure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".