The OMERACT First-time Participant (“Newbie”) Program: Initial Assessment and Lessons Learned
Bibliographic record
Abstract
OBJECTIVE: To describe the experience of a first-time participant ("newbie") training program at the Outcome Measures in Rheumatology 12 meeting in 2014. METHODS: We conducted newbie sessions at OMERACT 12, including a 2-hour introductory session on Day 1, followed by 1-h evening followup sessions on days 1-4 of OMERACT 12. Pre- and postmeeting surveys assessed participants' level of comfort with the principles of the OMERACT Filters 1.0 (truth, discrimination, feasibility), and Filter 2.0 (the essential tools for OMERACT methodology), the different types of OMERACT sessions, and whether participants felt welcome. RESULTS: In all, 25 new attendees participated in the introductory session and 10-16 attended followup sessions. Fewer participants reported being somewhat or extremely uncomfortable with the meeting, comparing Day 1 (preintroductory session) to days 1-4 (post): (1) with different OMERACT sessions: 56% (pre) versus 6%, 0%, 8%, and 6% (post days 1-4, respectively); and (2) with principles of the OMERACT filter, 64% (pre) versus 7%, 0%, 8%, and 0% (post), respectively. Most reported feeling welcome (100%) and that they were able to contribute substantively to breakout sessions (87%) on Day 1 evening; results were sustained on days 2-4. CONCLUSION: First-time participant training sessions increased the comfort level of the participants with the OMERACT meeting structure and filter, and increased the ability of the new attendees to feel they could contribute to the OMERACT process.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".