Matching the right study design to decision-maker questions: Results from a Delphi study
Bibliographic record
Abstract
Research evidence can play an important role in each stage of decision-making, evidence-support systems play a key role in aligning the demand for and supply of evidence. This paper provides guidance on what type of study designs most suitably address questions asked by decision-makers. This study used a two-round online Delphi approach, including methodological experts in different areas, disciplines, and geographic locations. Participants prioritized study designs for each of 40 different types of question, with a Kendall's W greater than 0.6 and reaching statistical significance (p<0.05) considered as a consensus. For each type of question, we sorted the final rankings based on their median ranks and interquartile ranges, and listed the four study designs with the highest median ranks. Participants provided 29 answers in the two rounds of the Delphi, and reached a consensus for 28 (out of the 40) questions (eight in the first round and 20 in the second). Participants achieved a consensus for 8 of 15 questions in stage I (clarifying a societal problem, its causes, and potential impacts), 12 of 13 in stage II (finding options to address a problem) and four of six in each of stages III (implementing or scaling-up an option) and IV (monitoring implementation and evaluating impact). This paper provides guidance on what study designs are more suitable to give insights on 28 different types of questions. Decision-makers, evidence intermediaries (, researchers and funders can use this guidance to make better decisions on what type of study design to commission, use or fund when answering specific needs.
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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 | 0.252 | 0.482 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| 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".