A Systematic review of macro - and meso - determinants of national health insurance enrolment among older adults in Ghana
Bibliographic record
Abstract
: Achieving universal health coverage (UHC) through the National Health Insurance Scheme (NHIS) has been a priority for Ghanaian governments. Despite the plethora of studies conducted to explore the various factors that influence enrolment into the scheme, there remains a dearth in the literature with regards to a systematic review of the health- and system-level determinants of NHIS enrolment among older adults in Ghana. This study aimed to synthesize evidence on macro- and meso-level determinants of NHIS enrolment among older adults in Ghana. With literature from data repositories including Wiley Web of Science, PubMed, PsycINFO, Scopus, Ovid, Science Direct and Sage, we performed a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Out of 124 studies screened, the systematic review included 11 articles. The study identified 4 macro- and 3 meso-determinants of national health insurance enrolment among older adults in Ghana. Macro-determinants identified were perceived scheme benefits, affordability, proximity to NHIS offices, quality of administrative service delivery. Physical accessibility, quality of care, and staff attitude were identified as meso-determinants. The study recommends improving physical accessibility, quality of care, and staff attitude. Additionally, it suggests addressing perceived scheme benefits and improving the quality of administrative service delivery.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".