Embracing unity at OMERACT: Valuing equity, promoting diversity, fostering inclusivity
Bibliographic record
Abstract
OBJECTIVE: To increase awareness and understanding of the principles of Equity, Diversity, and Inclusivity (EDI) within Outcome Measures in Rheumatology's (OMERACT) members. For this, we aimed to obtain ideas on how to promote and foster these principles within the organization and determine the diversity of the current membership in order to focus future efforts. METHODS: We held a plenary workshop session at OMERACT 2023 with roundtable discussions on barriers and solutions to increased diversity within OMERACT. We conducted an anonymous, web-based survey of members to record characteristics including population group, gender identity, education level, age, and ability. RESULTS: The workshop generated ideas to increase diversity of participants across the themes of building relationships [12 topics], materials and methods [5 topics], and conference-specific [6 topics]. Four hundred and seven people responded to the survey (25 % response rate). The majority of respondents were White (75 %), female (61 %), university-educated (94 %), Christian (42 %), spoke English at home (60 %), aged 35 to 55 years (50 %), and did not report a disability (64 %). CONCLUSION: OMERACT is committed to improving its diversity. Next steps include strategic recruitment of members to the EDI working group, drafting an EDI mission statement centering equity and inclusivity in the organization, and developing guidance for the OMERACT Handbook to help all working groups create actionable plans for promoting EDI principles.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".