Treatment-seeking Behaviour and Social Health Insurance in Africa: the Case of Ghana under the National Health Insurance Scheme
Bibliographic record
Abstract
Health insurance is attracting more and more attention as a means for improving health care utilization and protecting households against impoverishment from out-of-pocket expenditures. Currently about 52 percent of the resources for financing health care services come from out of pocket sources or user fees in Africa. Therefore, Ghana serves as in interesting case study as it has successfully expanded coverage of the National Health Insurance Scheme (NHIS). The study aims to establish the treatment-seeking behaviour of households in Ghana under the NHI policy. The study relies on household data collected from three districts in Ghana covering the 3 ecological zones namely the coastal, forest and savannah.Out of the 1013 who sought care in the previous 4 weeks, 60% were insured and 71% of them sought care from a formal health facility. The results from the multinomial logit estimations show that health insurance and travel time to health facility are significant determinants of health care demand. Overall, compared to the uninsured, the insured are more likely to choose formal health facilities than informal care including self-medication when ill. We discuss the implications of these results as the concept of the NHIS grows widely in Ghana and serves as a good model for other African countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".