Contributing to collaborative health governance in Africa: a realist evaluation of the Universal Health Coverage Partnership
Bibliographic record
Abstract
BACKGROUND: Policy dialogue, a collaborative governance mechanism, has raised interest among international stakeholders. They see it as a means to strengthen health systems governance and to participate in the development of health policies that support universal health coverage. In this context, WHO has set up the Universal Health Coverage Partnership. This Partnership aims to support health ministries in establishing inclusive, participatory, and evidence-informed policy dialogue. The general purpose of our study is to understand how and in what contexts the Partnership may support policy dialogue and with what outcomes. More specifically, our study aims to answer two questions: 1) How and in what contexts may the Partnership initiate and nurture policy dialogue? 2) How do collaboration dynamics unfold within policy dialogue supported by the Partnership? METHODS: We conducted a multiple-case study realist evaluation based on Emerson's integrative framework for collaborative governance to investigate the role of the Partnership in policy dialogue on three policy issues in six sub-Saharan African countries: health financing (Burkina Faso and Democratic Republic of Congo), health planning (Cabo Verde, Niger, and Togo), and aid coordination for health (Liberia). We interviewed 121 key informants, analyzed policy documents, and observed policy dialogue events. RESULTS: The Partnership may facilitate the initiation of policy dialogue when: 1) stakeholders feel uncertain about health sector issues and acknowledge their interdependence in responding to such issues, and 2) policy dialogue coincides with their needs and interests. In this context, policy dialogue enables stakeholders to build a shared understanding of issues and of the need for action and encourages collective leadership. However, ministries' weak ownership of policy dialogue and stakeholders' lack of confidence in their capacity for joint action hinder their engagement and curb the institutionalization of policy dialogue. CONCLUSIONS: Development aid actors wishing to support policy dialogue must do so over the long term so that collaborative governance becomes routine and a culture of collaboration has time to grow. Public administrations should develop collaborative governance mechanisms that are transparent and intelligible in order to facilitate stakeholder engagement.
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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 | 0.163 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".