Rating versus ranking in a Delphi survey: a randomized controlled trial
Bibliographic record
Abstract
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. Little is known about the effect of assessment procedure on the outcome of the Delphi process, questionnaire completion time, and evaluation of task difficulty. This dataset pertains to a randomized controlled parallel group trial that was embedded in a three-round online Delphi survey to compare rating and ranking. After an “open” first round, primary care patients, trained patient partners, and primary care clinicians (n=36) from seven primary care practices in Quebec, Canada, 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. Items achieving an initial consensus level ≥66.6% in each study group during round 2 were reassessed during the final (third) round. The dataset related to the main Delphi study, in which the results from the rating and ranking groups were combined and differences between patient and clinician panelists were explored, is available here: https://doi.org/10.6084/m9.figshare.20110280.v1. The study was approved by the University of Montreal Hospital Research Centre’s research ethics committee (project number 17.305). Participant recruitment and data collection took place over a one-year period, from November 2019 to November 2020. Please see the README_file for more information about the variables in the dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.131 | 0.168 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".