Drivers of Satisfaction With Care for Patients With Lupus
Bibliographic record
Abstract
OBJECTIVE: Quality of life (QOL) and quality of care (QOC) in systemic lupus erythematosus (SLE) remains poor. Satisfaction with care (SC), a QOC surrogate, correlates with health behaviors and outcomes. This study aimed to determine correlates of SC in SLE. METHODS: A total of 1262 patients with SLE were recruited from various countries. Demographics, disease activity (modified Systemic Lupus Erythematosus Disease Activity Index for the Safety of Estrogens in Lupus Erythematosus: National Assessment trial [SELENA-SLEDAI]), and QOL (LupusPRO version 1.7) were collected. SC was collected using LupusPRO version 1.7. Regression analyses were conducted using demographic, disease (duration, disease activity, damage, and medications), geographic (eg, China vs United States), and QOL factors as independent predictors. RESULTS: The mean (SD) age was 41.7 (13.5) years; 93% of patients were women. On the univariate analysis, age, ethnicity, current steroid use, disease activity, and QOL (social support, coping) were associated with SC. On the multivariate analysis, Asian participants had worse SC, whereas African American and Hispanic patients had better SC. Greater disease activity, better coping, and social support remained independent correlates of better SC. Compared with US patients, patients from China and Canada had worse SC on the univariate analysis. In the multivariate models, Asian ethnicity remained independently associated with worse SC, even after we adjusted for geographic background (China). No associations between African American or Hispanic ethnicity and SC were retained when geographic location (Canada) was added to the multivariate model. Canadian patients had worse SC when compared with US patients. Higher disease activity, better social support, and coping remained associated with better SC. CONCLUSION: Greater social support, coping, and, paradoxically, SLE disease activity are associated with better SC. Social support and coping are modifiable factors that should be addressed by the provider, especially in the Asian population. Therefore, evaluation of a patient's external and internal resources using a biopsychosocial model is recommended. Higher disease activity correlated with better SC, suggesting that the latter may not be a good surrogate for QOC or health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".