Increased investment in Universal Health Coverage in Sub–Saharan Africa is crucial to attain the Sustainable Development Goal 3 targets on maternal and child health
Bibliographic record
Abstract
Universal Health Coverage (UHC) is considered a strategic component of the Sustainable Development Goals specifically for goal 3 which seeks to ensure healthy lives and promote well-being for all, where all individuals and communities have equal access to key promotive, preventive, curative, and rehabilitative health interventions without financial constraints. Despite Sub-Saharan Africa (SSA) accelerated gains on the UHC effective coverage of 2.6% between 2010 to 2019, many countries in the sub-region show lagging performance. The major challenges faced in attaining the UHC in many countries include inadequate capital investment for health and their equitable distribution, fiscal space to finance UHC policies and programs. This paper discusses how increased investment in Universal Health Coverage in SSA is crucial to attain the Sustainable Development Goal 3 targets on maternal and child health. The Universal Health Monitoring Framework (UHMF) is adopted in this paper as the underpinning framework. The delivery of essential maternal and child health services to achieve UHC in SSA requires strategic actions such as policies, plans and programs with focus on maternal and child health. We report findings from recently published papers that clearly highlighted the strong connection between health insurance coverage and maternal health care utilization. Strategic actions such as implementing national health insurance scheme (NHIS) that directly incorporates free maternal and child health care could strengthen maternal health services and transform health systems in order to achieve UHC in SSA. We argue that achieving the SDG 3 on maternal and child health will only be possible if significant progress in made in increasing UHC. This is key to ensure optimal maternal health care utilization, and consequently reducing maternal and child deaths.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".