GHANAS NATIONAL HEALTH INSURANCE SCHEME: RETROSPECTIVE CASE REVIEW
Bibliographic record
Abstract
Abstract: Introduction: Several African countries have health insurance schemes or programs for their citizens and Ghana is one of such countries in Africa having taken bold steps to achieving such milestone. Programmes. These programmes seek to extend health insurance to the marginalised groups within the population and citizenry. The purpose of this article is to have retrospective review of the national health insurance scheme in Ghana, in relation to the restructured National Health Insurance Authority (NHIA) and their ability to serve all Ghanaians irrespective of one’s financial capabilities. Method: This study is a retrospective review using secondary data and records, reports as obtained from official manuals, from the former National Health Insurance Scheme (NHIS) of Ghana, published reports and data on their website as of 2018/19, world health organisation reports as of 2022 and manuals from the ministry of health Ghana and also reviews from works done by researchers of 2022 that used the National Health Insurance Scheme of Ghana as a case. This was done with search engines such as google scholar, world of science, academia.edu, Results: The authors of this work found that health insurance been operated by the government of Ghana was the dominant model in Ghana but with open space that has accommodated many private funded health insurance companies to operate within laid done regulations although the private health insurance was still marginal as of 2022 with reference to the population of Ghana in 2022. The review also confirmed the limitations of contribution-based financing and the need to strengthen tax-based financing in a way that will not burden the ordinary Ghanaian. Conclusion: The National Health Insurance Authority of Ghana is more likely to contribute to the achievement of universal health coverage (UHC) goals only if it ensures better management and enhance innovative way of adding resources on a larger scale while widening coverage on many diseases and treatment options with a unique feature of covering all primary health conditions and covering paediatric secondary conditions like some paediatric cancers and geriatric (old age) conditions with rolling out co - sharing premiums for diseases like renal failures or kidney diseases that warrant frequent dialysis for the working class.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".