Timing and utilisation of antenatal care service in Nigeria and Malawi
Bibliographic record
Abstract
As the world draws curtains on the implementation of Millennium Development Goals (MDGs), there is increasing interest in evaluating the performance of countries on the goals and assessing related challenges and opportunities to inform the upcoming Sustainable Development Goals (SDGs). This study examined changes in the timing and utilisation of maternal health care services in Nigeria and Malawi; using multivariate negative log-log and logistic regression models fitted to demographic and health survey data sets. Predicted probabilities were also computed to observe the net differences in the likelihood of both the first and the required number of antenatal care (ANC) visits for each of the three analysis years. Women in Nigeria were 7% less likely in 2008 compared to 2003, and in Malawi, 32% more likely in 2013 compared to 2000, to utilise ANC in the first trimester of pregnancy. Timing of first ANC visit was strongly influenced by wealth in Nigeria but not in Malawi. The findings in our case studies show how various contextual factors may enable or inhibit policy performance. Maternal and child health, SDGs should incorporate both wealth and degrees of urbanicity into country level implementation strategies.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".