The Comparability of English, French and Dutch Scores on the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F): An Assessment of Differential Item Functioning in Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: The Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) is commonly used to assess fatigue in rheumatic diseases, and has shown to discriminate better across levels of the fatigue spectrum than other commonly used measures. The aim of this study was to assess the cross-language measurement equivalence of the English, French, and Dutch versions of the FACIT-F in systemic sclerosis (SSc) patients. METHODS: The FACIT-F was completed by 871 English-speaking Canadian, 238 French-speaking Canadian and 230 Dutch SSc patients. Confirmatory factor analysis was used to assess the factor structure in the three samples. The Multiple-Indicator Multiple-Cause (MIMIC) model was utilized to assess differential item functioning (DIF), comparing English versus French and versus Dutch patient responses separately. RESULTS: A unidimensional factor model showed good fit in all samples. Comparing French versus English patients, statistically significant, but small-magnitude DIF was found for 3 of 13 items. French patients had 0.04 of a standard deviation (SD) lower latent fatigue scores than English patients and there was an increase of only 0.03 SD after accounting for DIF. For the Dutch versus English comparison, 4 items showed small, but statistically significant, DIF. Dutch patients had 0.20 SD lower latent fatigue scores than English patients. After correcting for DIF, there was a reduction of 0.16 SD in this difference. CONCLUSIONS: There was statistically significant DIF in several items, but the overall effect on fatigue scores was minimal. English, French and Dutch versions of the FACIT-F can be reasonably treated as having equivalent scoring metrics.
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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 | 0.018 |
| 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 | 0.001 |
| 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".