The OMERACT First-time Participant Program: Fresh Eye from the New Guys
Bibliographic record
Abstract
OBJECTIVE: To describe the experience of the first-time participant (newbie) training program at the Outcome Measures in Rheumatology (OMERACT) 2016 meeting. METHODS: We conducted new participant sessions at OMERACT 2016, including a 2-h introductory session on Day 1 followed by 1-h evening followup sessions on days 1-4. Pre- and post-meeting surveys assessed participants' levels of comfort with the principles of the OMERACT Filter 2.0 (the essential tools for OMERACT methodology) and the different types of OMERACT sessions, and whether participants felt welcome. In addition, on the final day, a nominal group technique was used to elicit problematic components of the meeting and to develop solutions to those problems. RESULTS: Of the 43 new attendees, 38 participated in the introductory session and 14-18 attended the followup sessions. Comparing Day 1 (preintroductory session) to days 1-3 (post), a similar proportion understood different types of sessions extremely well [45% (pre) versus 47%, 44%, and 36% (post), respectively], and a higher proportion understood principles of the OMERACT filter extremely well [22% (pre) versus 55%, 44%, and 40% (post), respectively]. Most reported feeling welcome (86.7%) and felt they contributed to breakout sessions (93.3%) on the evening of Day 1; results were sustained on days 2-3. The most commonly reported "best" experience included the OMERACT culture and the most common reported experience needing improvement included facilitation issues during breakouts. CONCLUSION: The first-time participants came to OMERACT 2016 with a high baseline level of understanding. They rapidly attained a high comfort level with participation and provided concrete and innovative solutions to the most commonly reported experiences needing improvement.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".