Using consensus group methods such as Delphi and Nominal Group in medical education research
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- Metaresearch, Science and technology studies, Insufficient payload (model declined to judge)
- Consensus categories
- Metaresearch
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Other designConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.904
- Threshold uncertainty score
- 0.999
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.282 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Consensus group methods are widely used in research to identify and measure areas where incomplete evidence exists for decision-making. Despite their widespread use, these methods are often inconsistently used and reported. Using examples from the three most commonly used methods, the Delphi, Nominal Group and RAND/UCLA; this paper and associated Guide aim to describe these methods and to highlight common weaknesses in methodology and reporting. The paper outlines a series of recommendations to assist researchers using consensus group methods in providing a comprehensive description and justification of the steps taken in their study.
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.
The record
- Venue
- Medical Teacher
- Topic
- Delphi Technique in Research
- Field
- Social Sciences
- Canadian institutions
- University of Ottawa
- Funders
- not available
- Keywords
- Nominal group techniqueNominal groupDelphi methodDelphiGroup (periodic table)Strengths and weaknessesMedical educationManagement scienceData scienceComputer sciencePsychologyMedicineKnowledge managementSocial psychologyEngineeringArtificial intelligence
- Has abstract in OpenAlex
- yes