COVID‐19 and progress towards achieving universal health coverage in Africa: A case of Nigeria
Bibliographic record
Abstract
Universal Health Coverage (UHC) 2030 is a global health target, and countries are making efforts to convert plans into tangible results. Nigeria, the most populated country in Africa, has made commitments towards UHC2030 target but is underperforming across many building blocks of health and progress has been slow. The arrival of COVID-19 poses additional pressure on the already feeble health system causing the government to direct focus towards containing the pandemic. However, existing gaps in health workforce density, weak primary health care infrastructure and inadequate budgetary allocation have resulted in inequitable access to basic healthcare services. This situation weighs most heavily on the poor who are mostly part of the informal economy thereby pushing people further into poverty. On the other hand, COVID-19 has provided valuable insights into Nigeria's current health system status which hopefully can be helpful in strengthening efforts towards building resilient health system and preparing the country towards future pandemic. The pandemic has highlighted the importance of essential health services and the need to strengthen primary healthcare system. It is, therefore, important that stakeholders in Nigeria and other African countries carry out situation analysis of the current health systems towards achieving UHC2030.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".