Aspirations and realities of intergovernmental collaboration in national- level interventions: insights from maternal, neonatal and child health policy processes in Nigeria, 2009–2019
Bibliographic record
Abstract
In Nigeria's federal government system, national policies assign concurrent healthcare responsibilities across constitutionally arranged government levels. Hence, national policies, formulated for adoption by states for implementation, require collaboration. This study examines collaboration across government levels, tracing implementation of three maternal, neonatal and child health (MNCH) programmes, developed from a parent integrated MNCH strategy, with intergovernmental collaborative designs, to identify transferable principles to other multilevel governance contexts, especially low-income countries.National-level setting was Abuja, where policymaking is domiciled, while two subnational implementation settings (Anambra and Ebonyi states) were selected based on their MNCH contexts. A qualitative case study triangulated information from 69 documents and 44 in-depth interviews with national and subnational policymakers, technocrats, academics and implementers. Emerson's integrated collaborative governance framework was applied thematically to examine how governance arrangements across the national and subnational levels impacted policy processes.The results showed that misaligned governance structures constrained implementation. Specific governance characteristics (subnational executive powers, fiscal centralisation, nationally designed policies, among others) did not adequately generate collaboration dynamics for collaborative actions. Collaborative signing of memoranda of understanding happened passively, but the contents were not implemented. Neither state adhered to programme goals, despite contextual variations, because of an underlying disconnect in the national governance structure.Collaboration across government levels could be better facilitated via full devolution of responsibilities by national authorities to subnational governments, with the national level providing independent evaluation and guidance only. Given the existing fiscal structure, innovative reforms which hold government levels accountable should be linked to fiscal transfers. Sustained advocacy and context-specific models of achieving distributed leadership across government levels are required across similar resource-limited countries. Stakeholders should be aware of what drivers are available to them for collaboration and what needs to be built within the system context.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".