Fiscal decentralization and devolved healthcare service availability outcomes in Kenya: Evidence from panel dynamic approach
Bibliographic record
Abstract
The study examined the effect of fiscal decentralization on healthcare availability outcomes from 23 Kenyan counties during the 2013–2022 devolution period. The results from different robust non-endogeneity econometric methods indicate a significant deleterious effect of fiscal decentralization on the availability of human and technical healthcare resources (number of medical personnel and number of hospital beds per 10,000 people). The study also reveals the significant beneficial role of county gross domestic product in enhancing the availability of healthcare resources. Nevertheless, the paper demonstrates that county revenue impedes realizing adequate availability of healthcare resources in Kenyan counties. The study results point to the need to implement proactive decentralized fiscal policy interventions to realize an efficient healthcare system where human and technical healthcare resources are available. Specifically, enacting policy interventions that target effective financial allocation toward infrastructural development and building human resource capacity could enhance overall healthcare availability at the grassroots level.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".