Ghana’s National Health Insurance Scheme: a national level investigation of members’ perceptions of service provision
Bibliographic record
Abstract
BACKGROUND: Ghana's National Health Insurance Scheme (NHIS), established into law in 2003 and implemented in 2005 as a 'pro-poor' method of health financing, has made great progress in enrolling members of the general population. While many studies have focused on predictors of enrolment this study offers a novel analysis of NHIS members' perceptions of service provision at the national level. METHODS: Using data from the 2008 Ghana Demographic Health Survey we analyzed the perceptions of service provision as indicated by members enrolled in the NHIS at the time of the survey (n = 3468; m = 1422; f = 2046). Ordinal Logistic Regression was applied to examine the relationship between perceptions of service provision and theoretically relevant socioeconomic and demographic variables. RESULTS: Results demonstrate that wealth, gender and ethnicity all play a role in influencing members' perceptions of NHIS service provision, distinctive from its influence on enrolment. Notably, although wealth predicted enrolment in other studies, our study found that compared to the poorest men and uneducated women, wealthy men and educated women were less likely to perceive their service provision as better/same (more likely to report it was worse). Wealth was not an important factor for women, suggesting that household gender dynamics supersede household wealth status in influencing perceptions. As well, when compared to Akan women, women from all other ethnic groups were about half as likely to perceive the service provision to be better/same. CONCLUSIONS: Findings of this study suggest there is an important difference between originally enrolling in the NHIS because one believes it is potentially beneficial, and using the NHIS and perceiving it to be of benefit. We conclude that understanding the nature of this relationship is essential for Ghana's NHIS to ensure its longevity and meet its pro-poor mandate. As national health insurance systems are a relatively new phenomenon in sub-Saharan Africa little is known about their long term viability; understanding user perceptions of service provision is an important piece of that puzzle.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".