Political economy analysis of universal health coverage and health financing reforms in low- and middle-income countries: the role of stakeholder engagement in the research process
Bibliographic record
Abstract
BACKGROUND: Progress towards universal health coverage (UHC) is an inherently political process. Political economy analysis (PEA) is gaining momentum as a tool to better understand the role of the political and economic dimensions in shaping and achieving UHC in different contexts. Despite the acknowledged importance of actors and stakeholders in political economy considerations, their role in the PEA research process beyond "study subjects" as potential cocreators of knowledge and knowledge users has been overlooked so far. We therefore aimed to review the approaches with reference to stakeholder engagement during the research process adopted in the current published research on the political economy of UHC and health financing reforms, and the factors favouring (or hindering) uptake and usability of PEA work. METHODS: We reviewed the literature to describe whether, when and how stakeholders were involved in the research process of studies looking at the political economy of UHC and health financing reforms, and to identify challenges and lessons learned on effective stakeholder engagement and research uptake. We used a standardized search strategy with key terms across several databases; we screened and included articles that focused on PEA and UHC. Additionally, we conducted a short survey of the authors of the included studies to complement the information retrieved. RESULTS: Fifty articles met the inclusion criteria and were included in the analysis. We found overall little evidence of systematic engagement of stakeholders in the research process, which focused mostly on the data collection phase of the research (i.e., key informant interviews). Our study identifies some reasons for the varying stakeholder engagement. Challenges include PEA requiring specific skills, a focus on sensitive issues, and the blurriness in researchers' and stakeholders' roles and the multiple roles of stakeholders as research participants, study subjects and research users. Among the approaches that might favour usability of PEA work, we identified early engagement, coproduction of research questions, local partners and personal contact, political willingness, and trust and use of prospective analysis. CONCLUSIONS: Stakeholder engagement and research uptake are multifaceted concepts and complex processes, particularly when applied to PEA. As such, stakeholder engagement in the research process of PEA of UHC and health financing reforms is limited and underreported. Despite the challenges, however, stakeholder engagement remains key to ensuring relevance, usability and research uptake of PEA studies. More efforts are required to ensure engagement at different stages of the research process and better reporting in published articles.
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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 | 0.034 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".