An exploratory study of the policy process and early implementation of the free NHIS coverage for pregnant women in Ghana
Bibliographic record
Abstract
BACKGROUND: Pregnant women were offered free access to health care through National Health Insurance (NHIS) membership in Ghana in 2008, in the latest phase of policy reforms to ensure universal access to maternal health care. During the same year, free membership was made available to all children (under-18). This article presents an exploratory qualitative analysis of how the policy of free maternal membership was developed and how it is being implemented. METHODS: The study was based on a review of existing literature - grey and published - and on a key informant interviews (n = 13) carried out in March-June 2012. The key informants included representatives of the key stakeholders in the health system and public administration, largely at national level but also including two districts. RESULTS: The introduction of the new policy for pregnant women was seen as primarily a political initiative, with limited stakeholder consultation. No costing was done prior to introduction, and no additional funds provided to the NHIS to support the policy after the first year. Guidelines had been issued but beyond collecting numbers of women registered, no additional monitoring and evaluation have yet been put in place to monitor its implementation. Awareness of the under-18 s policy amongst informants was so low that this component had to be removed from the final study. Initial barriers to access, such as pregnancy tests, were cited, but many appear to have been resolved now. Providers are concerned about the workload related to services and claims management but have benefited from increased financial resources. Users still face informal charges, and are reported to have responded differentially, with rises in antenatal care and in urban areas highlighted. Policy sustainability is linked to the survival of the NHIS as a whole. CONCLUSIONS: Ghana has to be congratulated for its persistence in trying to address financial barriers. However, many themes from previous evaluations of exemptions policies in Ghana have recurred in this study - particularly, the difficulties of getting timely reimbursement to facilities, of controlling charging of patients, and of reaching the poorest. This suggests that providing free care through a national health insurance system has not solved systemic weaknesses. The wider concerns about raising the quality of care, and ensuring that all supply-side and demand-side elements are in place to make the policy effective will also take a longer term and bigger commitment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".