Identifying implementation science research and policy priorities to advance universal health coverage: a multi-country modified Delphi study
Bibliographic record
Abstract
Despite efforts to advance universal health coverage (UHC) in different contexts, evidence gaps remain, and implementation science has been underused to address these gaps and determine 'what works'. The study aimed to establish a research agenda that could guide future research by identifying implementation science research priorities to advance UHC. A three-round modified Delphi study design with a multi-country panel was employed. Initial implementation science research gaps were identified from two scoping reviews conducted by our team, supplemented by 10 papers that we identified through a search of Medline and CINAHL databases. We generated 64 research gaps that were shared with 272 participants in Round I. Round I responses were analysed using descriptive statistics and a cut-off of 75% to move to Round II. Round I qualitative analysis resulted in an additional 15 research gaps and one new topic area. Based on Round I findings, an improved set of research gaps was shared in Round II. Quantitative data in Round II were analysed using the same approach as Round I, using an 85% cut-off point. Open-ended responses were analysed thematically. Round II research gaps were then presented in a virtual workshop. Results from the workshop were analysed using weighted ranking analysis. Round I response rate was 34.9% with 43 research gaps across 12 topic areas. Round II response rate was 77.9% with 42 gaps across 13 topic areas that passed to the virtual workshop. The workshop response rate was 39%. Through this process, the top 10 ranked implementation science research gaps were identified. Identified research gaps are focused on assessing equity in the delivery of health services and financial risk protection interventions. Future research will further contextualise this research agenda with country-level actors.
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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 | 0.217 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".