Evaluation of the Coping Strategies Questionnaire-Revised for fatigue in systemic sclerosis: a Scleroderma Patient-centred Intervention Network (SPIN) cohort study
Bibliographic record
Abstract
OBJECTIVES: To adapt and evaluate the Coping Strategies Questionnaire-Revised (CSQ-R), designed to assess pain coping, for assessing coping with fatigue in systemic sclerosis (SSc). METHODS: We adapted CSQ-R items for fatigue, and a panel of people with SSc verified content validity. Scleroderma Patient-centred Intervention Network Cohort participants completed the CSQ-R-Fatigue. We evaluated factor structure with confirmatory factor analysis (CFA), assessed differential item functioning (DIF) by English and French language and disease subtype, and evaluated internal consistency and test-retest reliability. RESULTS: 863 participants were included. Most were female (n=756; 88%), and 36% (n=308) had diffuse SSc. We replicated the 6-factor CSQ-R structure (Tucker-Lewis Index =0.95, Comparative Fit Index =0.97, Root Mean Square Error of Approximation =0.05). We found substantive DIF across multiple factors, however, for language and disease subtype (11 items on 6 factors for language, 10 items on 5 factors for subtype). Factor-score differences due to DIF by language and subtype were ≥0.20 standardised mean differences for 4 factors each. Test-retest reliability for factors based on intraclass correlation was between 0.68 [95% CI 0.58, 0.76] and 0.91 [95% CI 0.88, 0.93]; n=183. CONCLUSIONS: The CSQ-R-Fatigue may not be appropriate to assess coping with fatigue in SSc due to possible biases related to language and disease severity. An additional concern is that the CSQ-R-Fatigue focuses on psychological coping and does not assess active coping strategies. Research is needed to identify or develop tools to evaluate coping strategies for managing fatigue in SSc.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".