Disease‐related differential item functioning in the work instability scale for rheumatoid arthritis: Converging results from three methods
Bibliographic record
Abstract
OBJECTIVE: The 23-item Work Instability Scale for Rheumatoid Arthritis (RA-WIS) is a promising measure to assess risk for future work disability. Validated in both rheumatoid arthritis (RA) and osteoarthritis (OA), it has high potential for cross-disease applications. Our objective was to examine disease-related differential item functioning (DIF) in the RA-WIS. METHODS: Workers with RA (n = 120) or OA (n = 130) were recruited from 3 sites and completed a questionnaire consisting of demographic and health- and work-related variables, including the RA-WIS (range 0-23, where 23 = highest work instability). Multiple DIF detection methods were applied for comparability: 1) Mantel-Haenszel and Breslow-Day procedures, 2) hierarchical 3-step sequential logistic regression procedure, and 3) a 1-parameter item response theory approach (Rasch analysis). Both tests of significance (chi-square and F tests) and effect size statistics (Δ(MH) , ΔR(2) ) were assessed to confirm items demonstrating uniform or nonuniform DIF. A 2-step purification procedure was applied to establish a DIF-free conditioning variable (total RA-WIS score) for DIF analyses. The resultant impact of disease-related DIF at the scale level was also evaluated. RESULTS: All 3 DIF detection methods converged to reveal 3 RA-WIS items as having significant disease-related uniform DIF. Two items ("difficulty opening doors" and "pressure on hand") were more likely affirmed in RA, while 1 item ("very stiff") was more likely affirmed in OA. Overall, only a marginal impact at the scale level was found due to a small proportion of scale items exhibiting DIF and the bidirectional nature of DIF effects. CONCLUSION: RA-WIS scores can be directly compared between RA and OA without significant concerns for DIF-related measurement bias.
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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 | 0.050 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".