Engaging Stakeholders and Promoting Uptake of OMERACT Core Outcome Instrument Sets
Bibliographic record
Abstract
OBJECTIVE: While there has been substantial progress in the development of core outcomes sets, the degree to which these are used by researchers is variable. We convened a special workshop on knowledge translation at the Outcome Measures in Rheumatology (OMERACT) 2016 with 2 main goals. The first focused on the development of a formal knowledge translation framework and the second on promoting uptake of recommended core outcome domain and instrument sets. METHODS: We invited all 189 OMERACT 2016 attendees to the workshop; 86 attended, representing patient research partners (n = 15), healthcare providers/clinician researchers (n = 52), industry (n = 4), regulatory agencies (n = 4), and OMERACT fellows (n = 11). Participants were given an introduction to knowledge translation and were asked to propose and discuss recommendations for the OMERACT community to (1) strengthen stakeholder involvement in the core outcome instrument set development process, and (2) promote uptake of core outcome sets with a specific focus on the potential role of post-regulatory decision makers. RESULTS: We developed the novel "OMERACT integrated knowledge translation" framework, which formalizes OMERACT's knowledge translation strategies. We produced strategies to improve stakeholder engagement throughout the process of core outcome set development and created a list of creative and innovative ways to promote the uptake of OMERACT's core outcome sets. CONCLUSION: The guidance provided in this paper is preliminary and is based on the views of the participants. Future work will engage OMERACT groups, "post-regulatory decision makers," and a broad range of different stakeholders to identify and evaluate the most useful methods and processes, and to revise guidance accordingly.
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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 | 0.464 | 0.466 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".