Illness intrusiveness explains race-related quality-of-life differences among women with systemic lupus erythematosus
Bibliographic record
Abstract
Our objective was to investigate whether quality of life in systemic lupus erythematosus (SLE) differs across ethnoracial groups and to identify factors that may explain race-related differences. Self-administered questionnaire data from 335 White, 40 Black, and 30 Asian women with SLE were obtained from a multi-center database. Measures assessed illness intrusiveness, psychological well-being, depressive symptoms, musculoskeletal pain, and learned helplessness. Extent of SLE disease activity was indexed by self-reported functional-system involvement. Educational attainment was indicated by number of years in school. Principal-components analysis reduced the four psychosocial measures to a single factor score. This represented psychosocial well-being In path analysis. Psychosocial well-being differed significantly across the three groups, with Whites reporting the highest, and Blacks the lowest, levels. Path analysis indicated that illness intrusiveness accounted for this race-related difference. Although disease activity was significantly associated with psychosocial well-being, it did not differ across ethnoracial groups. Illness intrusiveness and educational attainment emerged as independent mediators of the race-related difference in psychosocial well-being. We conclude that race-related quality-of-life differences exist among women with SLE and are mediated independently by illness intrusiveness and educational attainment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".