Political economy and the pursuit of universal health coverage in Ghana: a case study of the National Health Insurance Scheme
Bibliographic record
Abstract
The road to universal health coverage depends on resources committed to the health sector. In many cases, the political structure and strength of advocacy play an important role in setting budgets for health. However, this has, until recently, not been of interest to health system researchers and policymakers. In this study, we document the political path to the establishment of the Ghana National Health Insurance Scheme (NHIS) as well as continuous political interest in the scheme. To achieve our objectives, we used qualitative data from interviews with key stakeholders. These include stakeholders instrumental in the design and establishment of the NHIS. We also reviewed party manifestoes from the two main political parties in the country. Promises relating to the NHIS were extracted from the various manifestos and analysed. Other documents that account for the design and implementation of the scheme were reviewed. We found that the establishment of the NHIS was down to political commitment and effective engagement with relevant stakeholders. It was considered a solution to the political promise to remove user fees and make healthcare accessible to all. A review of the manifestos shows that in almost every election year after the NHIS was established, there has been some promise related to improving the scheme. There were several policy propositions repeated in different election years. The findings imply that advocacy to get health financing on the political agenda is crucial. This should start from the development of party manifestos. It is important to also ensure that proposed party policies are consistent with national priorities in the medium to long term.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".