Rating versus ranking in a Delphi survey: a randomized controlled trial
Bibliographic record
Abstract
Abstract Background The Delphi technique has steeply grown in popularity in health research as a structured approach to group communication process. Rating and ranking are two different procedures commonly used to quantify participants’ opinions in Delphi surveys. We explored the influence of using a rating or ranking approach on item prioritization (main outcome), questionnaire completion time, and evaluation of task difficulty in a Delphi survey aimed at identifying priorities for the organization of primary cardiovascular care. Methods A randomized controlled parallel group trial was embedded in a three-round online Delphi survey. After an “open” first round, primary care patients, trained patient partners, and primary care clinicians from seven primary care practices were allocated 1:1 to a rating or ranking assessment group for the remainder of the study by stratified permuted block randomization, with strata based on participants’ gender and status. Agreement on item prioritization between the experimental groups was measured by calculating Krippendorff’s alpha reliability coefficient on the aggregate rank order of items in each group after the final round. Self-reported ease or difficulty with the assessment task was measured with the Single Ease Question. Results Thirty-six panelists (13 clinic patients, 7 patient partners, 16 clinicians; 60% females) were randomized to the rating (n = 18) or ranking (n = 18) group, with 30 (83%) completing all rounds. Both groups identified the same highest priorities from a set of 41 items, but significant discrepancies were found as early as the seventh top item. There was moderately strong agreement between the priority ordering of top items common to both groups (Krippendorff’s alpha = 0.811, 95% CI = 0.669–0.920). A 9-min mean difference to complete the third-round questionnaire in favor of the rating group failed to achieve statistical significance (p = 0.053). Ranking was perceived as more difficult (p < 0.001). Conclusions A rating or ranking procedure led to modestly similar item prioritization in a Delphi survey, but ranking was more difficult. This study should be replicated with a larger number of participants and with variations in the ranking and rating procedures. Trial registration Not applicable.
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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 | 0.098 | 0.127 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".