Utilisation of skilled birth attendants over time in Nigeria and Malawi
Bibliographic record
Abstract
Despite recent modest progress in reducing maternal and infant mortality rates in sub-Saharan Africa, Nigeria and Malawi were still in the top 20 countries with highest rates of mortalities globally in 2015. Utilisation of professional services at delivery - one of the indictors of MDG 5 - has been suggested to reduce maternal mortality by 50%. Yet, contextual, socio-cultural and economic factors have served as barriers to uptake of such critical service. In this paper, we examined the impact of residential wealth index on utilisation of Skilled Birth Attendant in Nigeria (2003, 2008 and 2013), and Malawi (2000, 2004 and 2010) using Demographic and Health Survey data sets. The findings from multivariate logistic regressions show that women in Nigeria were 23% less likely to utilise skilled delivery services in 2013 compared to 2003. In Malawi, women were 75% more likely to utilise skilled delivery services in 2010 than in 2000. Residential wealth index was a significant predictor of utilisation of skilled delivery services over time in both Nigeria and Malawi. These findings illuminate progress made - based on which we make recommendations for achievement of SDG-3: ensure healthy lives and promote well-being for all at all ages in Nigeria and Malawi, and similar context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".