Policy dialogue as a collaborative tool for multistakeholder health governance: a scoping study
Bibliographic record
Abstract
INTRODUCTION: Health system governance is the cornerstone of performant, equitable and sustainable health systems aiming towards universal health coverage. Global health actors have increasingly been using policy dialogue (PD) as a governance tool to engage with both state and non-state stakeholders. Despite attempts to frame PD practices, it remains a catch-all term for both health systems professionals and researchers. METHOD: We conducted a scoping study on PD. We identified 25 articles published in English between 1985 and 2017 and 10 grey literature publications. The analysis was guided by the following questions: (1) How do the authors define PD? (2) What do we learn about PD practices and implementation factors? (3) What are the specificities of PD in low-income and middle-income countries? RESULTS: The analysis highlighted three definitions of policy dialogue: a knowledge exchange and translation platform, a mode of governance and an instrument for negotiating international development aid. Success factors include the participants' continued and sustained engagement throughout all the relevant stages, their ability to make a constructive contribution to the discussions while being truly representative of their organisation and their high interest and stake in the subject. Prerequisites to ensuring that participants remained engaged were a clear process, a shared understanding of the goals at all levels of the PD and a PD approach consistent with the PD objective. In the context of development aid, the main challenges lie in the balance of power between stakeholders, the organisational or technical capacity of recipient country stakeholders to drive or contribute effectively to the PD processes and the increasingly technocratic nature of PD. CONCLUSION: PD requires a high level of collaborative governance expertise and needs constant, although not necessarily high, financial support. These conditions are crucial to make it a real driver of health system reform in countries' paths towards universal health coverage.
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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 | 0.141 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.030 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".