The Spirit of OMERACT: Q Methodology Analysis of Conference Characteristics Valued by Delegates
Bibliographic record
Abstract
OBJECTIVE: To identify the major features of OMERACT meetings as valued by frequent participants and to explore whether there are groups of participants with different opinions. METHODS: Using Q methodology (a qualitative and quantitative approach to grouping people according to subjective opinion), participants (who attended more than 1 OMERACT conference) sorted 66 statements relating to the "spirit of OMERACT" according to level of agreement across a normal distribution grid. Data were examined using Q factor analysis. RESULTS: Of 226 potential participants, 105 responded (46%). All participants highly ranked the focus on global standardization of methods, outcome measures, data-driven research, methodological discussion, and international collaboration. Four factors describing the "spirit of OMERACT" were identified: "Evidence not eminence" (n = 31) valued the data- and evidence-driven research above personality and status; "Collaboration and collegiality" (n = 19) valued the international and cross-stakeholder collaboration, interaction, and collegiality; "Equal voices, equal votes, common goals" (n = 12) valued equality in discussion and voting, with everyone striving toward the same goal; "principles and product, not process" (n = 8) valued the principles of focusing on outcome measures and the product of guiding clinical trials, but were unsure whether the process is necessary to reach this. The factors did not segregate different stakeholder groups. CONCLUSION: Delegates value different elements of OMERACT, and thus the "spirit of OMERACT" encompasses evidence-based research, collaboration, and equality, although a small group are unsure whether the process is necessary to achieve the end result. Q methodology may prove useful for conference organizers to identify their delegates' different needs to tailor conference content.
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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 | 0.074 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".