Validity, Reliability, and Differential Item Functioning of English and French Versions of the <scp>10‐Item Connor‐Davidson</scp> Resilience Scale in Systemic Sclerosis: A Scleroderma <scp>Patient‐Centered</scp> Intervention Network Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Some individuals with systemic sclerosis (SSc) report positive mental health, despite severe disease manifestations, which may be associated with resilience, but no resilience measure has been validated in SSc. This study was undertaken to assess the validity, reliability, and differential item functioning (DIF) between English- and French-language versions of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) in SSc. METHODS: Eligible participants were enrolled in the Scleroderma Patient-centered Intervention Network Cohort and completed the CD-RISC-10 between August 2022 and January 2023. We used confirmatory factor analysis (CFA) to evaluate the CD-RISC-10 factor structure and conducted DIF analysis across languages with Multiple Indicators Multiple Causes models. We tested convergent validity with another measure of resilience and measures of self-esteem and depression and anxiety symptoms. We assessed internal consistency and test-retest reliability using Cronbach's alpha and intraclass correlation coefficient (ICC). RESULTS: A total of 962 participants were included in this analysis. CFA supported a single-factor structure (Tucker-Lewis index = 0.99, comparative fit index = 0.99, root mean square error of approximation = 0.08 [90% confidence interval (90% CI) 0.07, 0.09]). We found no meaningful DIF. Internal consistency was high (α = 0.93 [95% CI 0.92, 0.94]), and we found that correlations with other measures of psychological functioning were moderate to large (|r| = 0.57-0.78) and confirmed study hypotheses. The scale showed good 1-2-week test-retest reliability (ICC 0.80 [95% CI 0.75, 0.85]) in a subsample of 230 participants. CONCLUSION: The CD-RISC-10 is a valid and reliable measure of resilience in SSc, with score comparability across English and French versions.
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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 | 0.006 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".