Universal Health Coverage and Facilitation of Equitable Access to Care in Africa
Bibliographic record
Abstract
Background: Universal Health Coverage (UHC) is achieved in a health system when all residents of a country are able to obtain access to adequate healthcare and financial protection. Achieving this goal requires adequate healthcare and healthcare financing systems that ensure financial access to adequate care. In Africa, accessibility and coverage of essential health services are very low. Many African countries have therefore initiated reforms of their health systems to achieve universal health coverage and are advanced in this goal. The aim of this paper is to examine the effects of UHC on equitable access to care in Africa. Methods: A systematic review guided by the Cochrane Handbook was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria (PRISMA). Studies were eligible for inclusion if 1- they clearly mention studying the effect of UHC on equitable access to care, and 2- they mention facilitating factors and barriers to access to care for vulnerable populations. To be included, studies had to be in English or French. In accordance with PRISMA guidelines, our systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on April 24, 2018 (registration number CRD42018092793). Results: In all 271 citations reviewed, 12 studies were eligible for inclusion. Although universal health coverage seems to increase the use of health services, shortages in human resources and medical supplies, socio-cultural barriers, physical inaccessibility, lack of education and information, decision-making power and gender-based autonomy, prenatal visits, previous experiences, and fear of caesarean delivery were still found to deter access to, and use of, health services. Discussion: Barriers to greater effectiveness of the UHC correspond to various non-financial barriers. There are no specific recommendations for these kinds of barriers. Generally, it is important for each country to research and identify contextual uncertainties in each of the communities of the territory. Afterwards, it will be necessary to put in place adapted strategies to correct these uncertainties, and thus to work towards a more efficient system of UHC, resulting in positive impacts on health outcomes.
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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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".