Identifying High‐Impact Solutions to Address Racial and Ethnic Health Disparities in Lupus: A Consensus‐Based Approach
Bibliographic record
Abstract
OBJECTIVE: We conducted formative research aimed at identifying solutions that address inequitable health outcomes in lupus due to adverse social determinants of health (SDoH). METHODS: We conducted a search for keywords, which provided insights into potential solutions and initiatives underway. An advisory panel of lupus experts iteratively reviewed the list of literature-scoped solutions in working sessions, filling knowledge gaps, which allowed for further defining and classifying solutions based on area of focus, feasibility, and impact. Seven-in-depth semistructured discussions and a modified Delphi survey approach were leveraged to align the advisory panel based on feasibility, impact, and costs of the proposed solutions. RESULTS: Thirty-three solutions were identified and classified into four key categories: financial safety net, patient education and shared decision-making, physician education, and other solutions. High-impact solutions that were prioritized included the following: "collecting granular information like patient-reported outcomes to provide personalized care and accelerate development of new products," "expanding Medicaid coverage via infrastructure," and "supporting people living with lupus in applying and getting approval for disability." CONCLUSION: Addressing health and health care disparities linked to negative SDoH is a key goal in the management of lupus, as disparities in outcomes can be stark. Increasing the visibility of potential solutions and aligning the community on top priorities can enable more efficient and effective contributions to health care equity and ultimately better health outcomes for people living with lupus.
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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 | 0.331 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.021 | 0.009 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".