Ghana's National Health Insurance Scheme in the Context of the Health MDGs – An Empirical Evaluation Using Propensity Score Matching
Bibliographic record
Abstract
In 2003 the Government of Ghana established a National Health Insurance Scheme (NHIS) to improve health care access for Ghanaians and eventually replace the cash-and-carry system. This study evaluates the NHIS to determine whether it is fulfilling its purpose in the context of the Millennium Development Goals #4 and #5 which deal with the health of women and children. We use Propensity Score Matching techniques to balance the relevant background characteristics in our survey data and compare health outcomes of recent mothers who are enrolled in the NHIS with those who are not. Our findings suggest that NHIS women are more likely to receive prenatal care, deliver at a hospital, have their deliveries attended by trained health professionals, and experience less birth complications. We conclude that NHIS is an effective tool for increasing health care access, and improving health outcomes.
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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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".