Acceptability of an online modified Delphi panel approach for developing health services performance measures: results from 3 panels on arthritis research
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Online modified Delphi (OMD) panel approaches can be used to engage large and diverse groups of clinical experts and stakeholders in developing health services performance measures. Such approaches are increasing in popularity among health researchers. However, information about their acceptability to participating experts and stakeholders is lacking but important to determine before recommending widespread use of online approaches. Therefore, the objective of this paper is to explore acceptability of the OMD panel approach from the participants' perspective. METHOD: We use data from participants in three OMD panels designed to develop performance measures for use in arthritis research and quality improvement efforts. At the end of each online panel, we surveyed clinical experts and stakeholders who shared their experiences with the OMD process by answering 13 close-ended questions using 7-point Likert-type scales. A mean of 5 or higher on a given question was treated as an indication of acceptability. RESULTS: Ninety-eight clinical experts and stakeholders (92% participation rate) answered survey questions about the online process. They considered the OMD panel approach to be acceptable, particularly the ease of using the online system (mean = 5.3, standard deviation = 1.3) and the understanding gained from online discussions (mean = 5.2, standard deviation = 1.0). Participants also felt that participation in the Delphi study was interesting (mean = 5.6, standard deviation =1.1). CONCLUSION: These findings illustrate likely acceptability and a potential for a more widespread use of OMD panel approaches by stakeholders in developing health services performance measures.
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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 | 0.208 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".