Development of a Draft Core Set of Domains for Measuring Shared Decision Making in Osteoarthritis: An OMERACT Working Group on Shared Decision Making
Bibliographic record
Abstract
OBJECTIVE: Despite the importance of shared decision making for delivering patient-centered care in rheumatology, there is no consensus on how to measure its process and outcomes. The aim of this Outcome Measures in Rheumatology (OMERACT) working group is to determine the core set of domains for measuring shared decision making in intervention studies in adults with osteoarthritis (OA), from the perspectives of patients, health professionals, and researchers. METHODS: We followed the OMERACT Filter 2.0 method to develop a draft core domain set by (1) forming an OMERACT working group; (2) conducting a review of domains of shared decision making; and (3) obtaining opinions of all those involved using a modified nominal group process held at a session activity at the OMERACT 12 meeting. RESULTS: In all, 26 people from Europe, North America, and Australia, including 5 patient research partners, participated in the session activity. Participants identified the following domains for measuring shared decision making to be included as part of the draft core set: (1) identifying the decision, (2) exchanging information, (3) clarifying views, (4) deliberating, (5) making the decision, (6) putting the decision into practice, and (7) assessing the effect of the decision. Contextual factors were also suggested. CONCLUSION: We proposed a draft core set of shared decision-making domains for OA intervention research studies. Next steps include a workshop at OMERACT 13 to reach consensus on these proposed domains in the wider OMERACT group, as well as to detail subdomains and assess instruments to develop a core outcome measurement set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.203 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".