Reliability and Validity of the Turkish Version of the Arthritis-work Spillover Scale in Individuals with Rheumatoid Arthritis
Bibliographic record
Abstract
Objective: The aim of this study is to adapt the arthritis-work spillover (AWS) scale for Turkish and to examine its validity and reliability. Methods: The study included 60 individuals with rheumatoid arthritis. AWS scale, disabilities of the arm, shoulder and hand questionnaire (DASH) and its subscale DASH-work module (DASH-W), arthritis impact measurement scales (AIMS2), disease activity score 28 (DAS28), and Canadian occupational performance measure (COPM) were administered to the participants. Internal consistency analysis, Cronbach’s alpha coefficient, test-retest method, confirmatory factor analysis, convergent validity were used for validity and reliability analysis. Results: Cronbach’s alpha coefficient was used for internal consistency and the result was 0.86. The test-retest reliability coefficient was 0.68 (p<0.05). In the convergent validity analysis, moderately significant correlations were observed between the AWS and DASH-W (r=0.528, p<0.05), AIMS2-role (r=0.486, p<0.05), COPM-performance (r=-0.416, p<0.05) and COPM-satisfaction scores (r=-0.435, p<0.05). The AWS demonstrated good structural fit. High correlations were observed between AWS and AIMS2-symptom, moderate correlations with DASH, AIMS2-physical, AIMS2-affect, and low correlations with DAS28 (p<0.05). Conclusion: The results of this study showed that the Turkish version of the AWS was valid and reliable evaluation. The AWS scale should be used in clinics by clinicians such as physiotherapists, physicians, occupational therapists and psychologists etc. to determine the problems experienced by patients in their professional lives and could also be a guide in planning work and occupational programmes of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".