Validation of the Social Interaction Anxiety Scale in scleroderma: a Scleroderma Patient-centered Intervention Network Cohort study
Bibliographic record
Abstract
Introduction: Individuals with visible differences due to medical conditions, such as systemic sclerosis (SSc; scleroderma), have reported difficulty navigating social situations because of issues such as staring, invasive questions, and rude comments. Fears or anxiety linked to situations in which a person interacts with others is known as social interaction anxiety. However, there exists no validated measurement tool to examine social interaction anxiety in rheumatologic conditions. Methods: The present study examines the reliability (internal consistency) and validity (structural and convergent) of the Social Interaction Anxiety Scale-6 (SIAS-6) in a sample of 802 individuals with SSc, and compares these psychometric properties across limited and diffuse subtypes of the disease. Multi-group confirmatory factor analysis was used to examine the factor structure of the SIAS-6 in patients with both limited and diffuse SSc. Results: A one-factor structure was found to fit well for individuals with SSc with both limited and diffuse disease. The measure demonstrated strong internal consistency reliability and convergent validity with relevant measures in expected magnitudes and directions. Conclusions: The SIAS-6 is a psychometrically robust measure that can confidently be used in SSc populations to examine social interaction anxiety. Moreover, scores can meaningfully be compared between patients with limited and diffuse disease.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".