Health Financing Functions in Community Based Health Insurance Schemes and Health Equity in Kenya
Bibliographic record
Abstract
The aim of this study was to establish how the healthcare financing functions are modeled within CBHIs and how they have impacted on realization of health equity with government stewardship being treated as the moderating factor. The study adopted descriptive and explanatory research designs to collect data from four members each management team of all registered CBHIs in Kenya. Descriptive statistics, factor analysis, path analysis and multivariate regression analysis in terms of structural modeling equation (SEM) were conducted to determine the hypothesized relationships between the health financing functions and their impact on health equity in Kenya. The study shows that enrolment and strategic purchasing in CBHIs accounted for variation in health equity in terms of increasing access to quality healthcare services. With the introduction of government stewardship as the moderating factor, the variation of health equity accounted for by enrolment and strategic purchasing increased. It was therefore inferred that the government should define the place of CBHIs within the context of the national health financing policy for realization of health equity by instituting the necessary legal and regulatory framework.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".