Evaluating the sub-national fidelity of national Initiatives in decentralized health systems: Integrated Primary Health Care Governance in Nigeria
Bibliographic record
Abstract
BACKGROUND: Policy making, translation and implementation in politically and administratively decentralized systems can be challenging. Beyond the mere sub-national acceptance of national initiatives, adherence to policy implementation processes is often poor, particularly in low and middle-income countries. In this study, we explore the implementation fidelity of integrated PHC governance policy in Nigeria's decentralized governance system and its implications on closing implementation gaps with respect to other top-down health policies and initiatives. METHODS: Having engaged policy makers, we identified 9 core components of the policy (Governance, Legislation, Minimum Service Package, Repositioning, Systems Development, Operational Guidelines, Human Resources, Funding Structure, and Office Establishment). We evaluated the level and pattern of implementation at state level as compared to the national guidelines using a scorecard approach. RESULTS: Contrary to national government's assessment of level of compliance, we found that sub-national governments exercised significant discretion with respect to the implementation of core components of the policy. Whereas 35 and 32% of states fully met national criteria for the structural domains of "Office Establishment" and Legislation" respectively, no state was fully compliant to "Human Resource Management" and "Funding" requirements, which are more indicative of functionality. The pattern of implementation suggests that, rather than implementing to improve outcomes, state governments may be more interested in executing low hanging fruits in order to access national incentives. CONCLUSIONS: Our study highlights the importance of evaluating implementation fidelity in providing evidence of implementation gaps towards improving policy execution, particularly in decentralized health systems. This approach will help national policy makers identify more effective ways of supporting lower tiers of governance towards improvement of health systems and outcomes.
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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 | 0.051 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".