Cross-Language Measurement Equivalence of the Center for Epidemiologic Studies Depression (CES-D) Scale in Systemic Sclerosis: A Comparison of Canadian and Dutch Patients
Bibliographic record
Abstract
OBJECTIVES: Increasingly, medical research involves patients who complete outcomes in different languages. This occurs in countries with more than one common language, such as Canada (French/English) or the United States (Spanish/English), as well as in international multi-centre collaborations, which are utilized frequently in rare diseases such as systemic sclerosis (SSc). In order to pool or compare outcomes, instruments should be measurement equivalent (invariant) across cultural or linguistic groups. This study provides an example of how to assess cross-language measurement equivalence by comparing the Center for Epidemiologic Studies Depression (CES-D) scale between English-speaking Canadian and Dutch SSc patients. METHODS: The CES-D was completed by 922 English-speaking Canadian and 213 Dutch SSc patients. 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 the amount of 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 items on the CES-D. The English-speaking Canadian sample endorsed more feeling-related symptoms, whereas the Dutch sample endorsed more somatic/retarded activity symptoms. The overall estimate in depression scores between English and Dutch was not influenced substantively by DIF. CONCLUSIONS: CES-D scores from English-speaking Canadian and Dutch SSc patients can be compared and pooled without concern that measurement differences may substantively influence results. The importance of assessing cross-language measurement equivalence in rheumatology studies prior to pooling outcomes obtained in different languages should be emphasized.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".