Evaluation of Measurement Properties and Differential Item Functioning in the English and French Versions of the University of California, Los Angeles, Loneliness Scale‐6: A Scleroderma <scp>Patient‐Centered</scp> Intervention Network (<scp>SPIN</scp>) Study
Bibliographic record
Abstract
Objective Loneliness has been associated with poorer health‐related quality of life but has not been studied in patients with systemic sclerosis (SSc). The current study was undertaken to examine and compare the psychometric properties of the English and French versions of the University of California, Los Angeles, Loneliness Scale‐6 (ULS‐6) in patients with SSc during the COVID‐19 pandemic. Methods This study used baseline cross‐sectional data from 775 adults enrolled in the Scleroderma Patient‐Centered Intervention Network (SPIN) COVID‐19 Cohort. Reliability and validity of ULS‐6 scores overall and between languages were evaluated using confirmatory factor analysis (CFA), differential item functioning (DIF) through the multiple‐indicator multiple‐cause (MIMIC) model, omega/alpha calculation, and correlations of hypothesized convergent relationships. Results CFA for the total sample supported the single‐factor structure (comparative fit index [CFI] 0.96, standardized root mean residual [SRMR] 0.03), and all standardized factor loadings for items were large (0.60–0.86). The overall MIMIC model with language as a covariate fit well (CFI 0.94, SRMR 0.04, root mean square error of approximation 0.11). Statistically significant DIF was found for 3 items across language (βitem2 = 0.14, P < 0.001; βitem4 = –0.07, P = 0.01; βitem6 = 0.13, P < 0.001), but these small differences were without practical measurement implications. Analyses demonstrated high internal consistency with no language‐based convergent validity differences. Conclusion Analyses demonstrated evidence of acceptable reliability and validity of ULS‐6 scores in English‐ and French‐speaking adults with SSc. DIF analysis supported use of the ULS‐6 to examine comparative experiences of loneliness without adjusting for language.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".