Toward Ensuring Health Equity: Readability and Cultural Equivalence of OMERACT Patient-reported Outcome Measures
Bibliographic record
Abstract
OBJECTIVE: The goal of the Outcome Measures in Rheumatology (OMERACT) 12 (2014) equity working group was to determine whether and how comprehensibility of patient-reported outcome measures (PROM) should be assessed, to ensure suitability for people with low literacy and differing cultures. METHODS: The English, Dutch, French, and Turkish Health Assessment Questionnaires and English and French Osteoarthritis Knee and Hip Quality of Life questionnaires were evaluated by applying 3 readability formulas: Flesch Reading Ease, Flesch-Kincaid grade level, and Simple Measure of Gobbledygook; and a new tool, the Evaluative Linguistic Framework for Questionnaires, developed to assess text quality of questionnaires. We also considered a study assessing cross-cultural adaptation with/without back-translation and/or expert committee. The results of this preconference work were presented to the equity working group participants to gain their perspectives on the importance of comprehensibility and cross-cultural adaptation for PROM. RESULTS: Thirty-one OMERACT delegates attended the equity session. Twenty-six participants agreed that PROM should be assessed for comprehensibility and for use of suitable methods (4 abstained, 1 no). Twenty-two participants agreed that cultural equivalency of PROM should be assessed and suitable methods used (7 abstained, 2 no). Special interest group participants identified challenges with cross-cultural adaptation including resources required, and suggested patient involvement for improving translation and adaptation. CONCLUSION: Future work will include consensus exercises on what methods are required to ensure PROM are appropriate for people with low literacy and different cultures.
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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 | 0.301 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".