Implementing the PEIR Framework and PEIRS-22 to facilitate improved and sustainable patient engagement in OMERACT
Bibliographic record
Abstract
• The PEIRS-22 was used to measure the degree of meaningful patient engagement in OMERACT. • The PEIRS-22 has helped OMERACT pinpoint areas of patient engagement for focused improvements. • Feedback from OMERACT PRPs supports the validity of PEIRS-22 with no floor or ceiling effects. • Key areas of meaningful engagement for improvement include Procedural Requirements, Feeling Valued, and Benefits. OMERACT (Outcome Measures in Rheumatology) is an international initiative focused on improving outcome measurement in rheumatology research, fostering collaboration among PRPs, clinicians, and researchers to develop Core Outcome Sets. The 22-item Patient Engagement In Research Scale (PEIRS-22) is a tool designed to measure the level of meaningful patient engagement and guide efforts towards improvement. 1) To describe the current profile of patient engagement at OMERACT using the scores generated by the PEIRS-22 and 2) to assess the validity of the PEIRS-22 within the OMERACT group of PRPs. We administered the PEIRS-22 to assess the level of meaningful engagement of PRPs with OMERACT. We compared the scores with self-rated participant engagement, and asked open ended questions to investigate the validity of the tool in the OMERACT PRP population. Overall engagement was meaningful and correlated to self-reported level of engagement. However, there were components and items that were flagged as priorities for improvement (Convenience, Benefits and Team Environment, and specifically items PR11: I participated in making decisions about the project, T2: I was an equal partner in the research project team, and SU1: I received sufficient support to contribute to the project. This study highlights the validity of the PEIRS-22 within OMERACT and reveals satisfactory levels of meaningful PRP engagement. As OMERACT continues to learn and evolve, the PEIRS-22 will be integral in developing a structured and consistent approach to patient engagement.
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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 | 0.101 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".