The Impact of Panel Composition and Topic on Stakeholder Perspectives: Generating Hypotheses from Online Maternal and Child Health Modified-Delphi Panels
Bibliographic record
Abstract
Abstract Background: Multi-stakeholder engagement is crucial for conducting health services research. Delphi-based methodologies combining iterative rounds of questions with feedback on and discussion of group results are a well-documented approach to multi-stakeholder engagement. The aim of this study is to develop hypotheses about the impact of panel composition and topic on the propensity and meaningfulness of response changes in multi-stakeholder modified-Delphi panels.Methods: We conducted three online modified-Delphi multi-stakeholder panels using the same protocol. We assigned 60 maternal and child health professionals to a homogeneous (professionals-only) panel, 60 pregnant or postpartum women (patients) to a homogeneous panel, and 30 professionals and 30 patients to a mixed panel. In Round 1, participants rated seriousness of 11 maternal and child health outcomes using 0-100 scale and explained their ratings. In Round 2, participants saw Round 1 results and discussed them using anonymous, moderated online discussion boards. In Round 3, participants revised their original ratings. Our outcome measures included binary indicators of response changes to ratings of low, medium, and high severity maternal and child health outcomes and their meaningfulness, measured by a change of 10 or more points on a 0-100 scale.Results: Participants changed 55% of responses; the majority of response changes were meaningful. We developed three main hypotheses. First, stakeholders may be more likely to change their responses on preference-sensitive topics where there is a range of viable alternatives or perspectives. Second, patients may be more likely to change their responses and to do so meaningfully in mixed panels, whereas professionals may be more likely to do so in homogeneous panels. Third, the association between panel composition and response change may vary according to the topic.Conclusions: Results of our work not only helped generate empirically-derived hypotheses to be tested in future research, but also offer practical recommendations for designing multi-stakeholder online modified-Delphi panels.Registration: International Registered Report Identifier: DERR1-10.2196/16478
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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 | 0.300 | 0.518 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".