A Pilot Study in the Use of the Delphi Method to Document Conference Proceedings: Comparison of the Rate of Consensus Among Attending and Nonattending Participants
Bibliographic record
Abstract
Abstract Objective: While many medical practitioners value the interactive nature of in-person conferences, results of these interactions are often poorly documented. The objective of this study was to pilot the Delphi method for developing consensus following a national conference and to compare the results between experts who did and did not attend. Methods: A 3-round Delphi included experts attending the 2023 Society of Disaster Medicine and Health Preparedness Annual Meeting and experts who were members of the society but did not attend. Conference speakers provided statements related to their presentations. Experts rated the statements on a 1–7 scale for agreement using STAT59 software (STAT59 Services Ltd, Edmonton, Alberta, Canada). Consensus was defined as a standard deviation of ≤ 1.0. Results: Seventy-five statements were rated by 27 experts who attended and 10 who did not: 2634 ratings in total. There was no difference in the number of statements reaching consensus in the attending group (26/75) versus that of the nonattending group (27/75) (P = 0.89). However, which statements reached consensus differed between the groups. Conclusion: The Delphi method is a viable method to document consensus from a conference. Advantages include the ability to involve large groups of experts, statistical measurement of the degree of consensus, and prioritization of the results.
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 | 0.094 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".