An Assessment of the Measurement Equivalence of English and French Versions of the Center for Epidemiologic Studies Depression (CES-D) Scale in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVES: Center for Epidemiologic Studies Depression (CES-D) Scale scores in English- and French-speaking Canadian systemic sclerosis (SSc) patients are commonly pooled in analyses, but no studies have evaluated the metric equivalence of the English and French CES-D. The study objective was to examine the metric equivalence of the CES-D in English- and French-speaking SSc patients. METHODS: The CES-D was completed by 1007 English-speaking and 248 French-speaking patients from the Canadian Scleroderma Research Group Registry. Confirmatory factor analysis (CFA) was used to assess the factor structure in both samples. The Multiple-Indicator Multiple-Cause (MIMIC) model was utilized to assess differential item functioning (DIF). RESULTS: A two-factor model (Positive and Negative affect) showed excellent fit in both samples. Statistically significant, but small-magnitude, DIF was found for 3 of 20 CES-D items, including items 3 (Blues), 10 (Fearful), and 11 (Sleep). Prior to accounting for DIF, French-speaking patients had 0.08 of a standard deviation (SD) lower latent scores for the Positive factor (95% confidence interval [CI]-0.25 to 0.08) and 0.09 SD higher scores (95% CI-0.07 to 0.24) for the Negative factor than English-speaking patients. After DIF correction, there was no change on the Positive factor and a non-significant increase of 0.04 SD on the Negative factor for French-speaking patients (difference = 0.13 SD, 95% CI-0.03 to 0.28). CONCLUSIONS: The English and French versions of the CES-D, despite minor DIF on several items, are substantively equivalent and can be used in studies that combine data from English- and French-speaking Canadian SSc patients.
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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 | 0.030 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".