Designing an intervention for women with systemic lupus erythematosus from medically underserved areas to improve care: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (lupus) disproportionately affects women, racial/ethnic minorities and low-income populations. We held focus groups for women from medically underserved communities to discuss interventions to improve care. METHODS: From our Lupus Registry, we invited 282 women, ≥18 years, residing in urban, medically underserved areas. Hospital-based clinics and support groups also recruited participants. Women were randomly assigned to three focus groups. Seventy-five-minute sessions were recorded, transcribed and coded thematically using interpretative phenomenologic analysis and single counting methods. We categorized interventions by benefits, limitations, target populations and implementation questions. RESULTS: Twenty-nine women with lupus participated in three focus groups, (n = 9, 9, 11). 80% were African American and 83% were from medically underserved zip codes. Themes included the desire for lupus education, isolation at the time of diagnosis, emotional and physical barriers to care, and the need for assistance navigating the healthcare system. Twenty of 29 participants (69%) favored a peer support intervention; 17 (59%) also supported a lupus health passport. Newly diagnosed women were optimal intervention targets. Improvements in quality of life and mental health were proposed outcome measures. CONCLUSION: Women with lupus from medically underserved areas have unique needs best addressed with an intervention designed through collaboration between community members and researchers.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".