EFFECT OF FISCAL DECENTRALIZATION ON HEALTH OUTCOMES IN KENYA
Bibliographic record
Abstract
Health system in Kenya was devolved in March 2013 whereby the national government retained oversight and regulatory functions while county government were assigned curative, preventive, and promotive heath service within their jurisdiction. Countries such as USA, Australia, Canada, India and Nigeria have had mixed health outcomes after devolution while others have had to reverse devolution of healthcare governance. Following decentralization of healthcare governance in Kenya, investment into healthcare has risen significantly. However, the actual impact on key health outcomes in Kenya is yet to be determined objectively. The research aimed to evaluate the effect of fiscal decentralization on health outcomes in Kenya. An analytical design was adopted. Secondary data was obtained from annual economic survey reports and Statistics. Abstract obtained from Kenya National Bureau of Statistics was used to obtain data that was analysed to identify trends in health outcomes before and after decentralization of healthcare governance. Descriptive statistics was used to describe the trends. Data was presented in form of line graphs, histograms and tables. Results indicated that fiscal decentralization has negative and significant effect on immunization coverage and skilled delivery. Further political, civil service and fiscal decentralization was recommended. Key Words: Fiscal Decentralization, Health Outcomes CITATION: Mbogori, M. M., & Iravo, M. A. (2019). Effect of fiscal decentralization on health outcomes in Kenya. The Strategic Journal of Business & Change Management, 6 (2), 1953 –1963.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".