Ghana’s National Health Insurance enrollment: Does the intersection of educational and residential status matter?
Bibliographic record
Abstract
BACKGROUND: Since its inception in 2003, Ghana's Health Insurance Scheme (NHIS) has received considerable scholarly attention on the determinants of enrollment. While most of these studies highlight the role of some socioeconomic and geographical factors, no study has explored the intersection of educational attainment and residence on NHIS enrollment. We aim to contribute to the literature and health policy in Ghana by examining the intersection of educational attainment and rural-urban residence on NHIS enrollment among women and men. METHODS: We used nationally representative data from the 2022 Ghana Demographic and Health Survey (GDHS). Using STATA 17, we applied multivariable logistic regression to our analytical sample comprising women (n = 14997) and men (n = 7040). RESULTS: Overall, we found that more women (90%) than men (73%) enrolled on the NHIS. Adjusting for a range of control variables, we found that women and men with secondary (OR: 1.61, 95% CI: 1.28-2.02; OR: 1.45, 95% CI: 1.16-1.82) and higher education (OR: 1.81, 95% CI: 1.24-2.64; OR: 2.85, 95% CI: 2.03-3.99) were more likely to have enrolled into the NHIS compared to those with no formal education. This difference was particularly heightened among women and men with no education. Rural women (96%) and men (90%) with higher education had higher enrollment rates compared to their urban counterparts. CONCLUSION: We recommend revising the NHIS equity and pro-poor policy to include vulnerability at the intersection of low educational attainment and rural residence.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".