Implementation science research priorities for Universal Health Coverage: methodological lessons from the design and implementation of a multicountry modified Delphi study
Bibliographic record
Abstract
Delphi studies are rapidly gaining prominence in global health research. However, researchers' modifications to the Delphi method are often not well-described or justified, limiting opportunities to systematically learn from these studies when the methods are applied to other topics and settings. This paper aims to describe an approach to implementing a modified Delphi study and reflect on the research process in the context of a multicountry study of implementation science research priorities to advance Universal Health Coverage (UHC). We review trends in the use of the modified Delphi method in global health research, outline our three-phased modified Delphi approach, and share reflections on five decision points for implementing the study: (I) identifying and recruiting participants for the expert panel, (II) addressing participant attrition between rounds, (III) justifying the most appropriate cutoff points, (IV) incorporating new items raised by participants in open-ended survey sections, and (V) ensuring maximum variation in perspective in the panel of experts. Insights from this work foster greater understanding of the underlying assumptions for, and interpretation of, 'modified' in modified Delphi studies. This study will encourage critical dialogue about points of methodological contention in Delphi methodology and thus are relevant for scaling the use of modified Delphi studies in public health, including global health research.
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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 | 0.618 | 0.584 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.007 | 0.014 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".