Including Health Equity Considerations in Development of Instruments for Rheumatology Research: An Introduction to a Novel OMERACT Paradigm
Bibliographic record
Abstract
The Outcome Measures in Rheumatology (OMERACT) Equity Special Interest Group (SIG) was established in 2008 to create a preliminary core set of outcome measures for clinical trials that can assess equity gaps in healthcare and the effectiveness of interventions to close or narrow gaps between advantaged and disadvantaged populations with musculoskeletal (MSK) conditions. At the OMERACT 11 meeting in 2012, the Equity SIG workshop focused on health assessment scales and their applicability for disadvantaged patients with MSK conditions. The intent was to determine whether the items and domains in 2 common questionnaires, the Health Assessment Questionnaire and the Medical Outcome Study Short Form-36 Survey, are appropriate for the activities and life experiences of certain disadvantaged populations, and whether completion of any of the scales would present a challenge to disadvantaged persons. To generate discussion, we considered the reading level of items in these questionnaires and whether they would be accessible to people with different levels of literacy. The group concluded that the choice of measurement instrument may contribute to "outcome measure-generated inequalities" because disadvantaged groups might have difficulty understanding some of the questions. The future work of the Equity SIG will explore the appropriateness of different measurement scales as they relate to inequities in arthritis as well as the risk of exacerbating disadvantages for patients with low literacy.
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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 | 0.440 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.007 | 0.021 |
| 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; 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".