An ecological approach to understanding and addressing health inequities of systemic lupus erythematosus
Bibliographic record
Abstract
Systemic Lupus Erythematosus (SLE) is a complex chronic autoimmune disease disproportionally afflicting women and, in particular, American Indian/Alaska Native, Black, and Hispanic women. These groups of women have significantly worse SLE-related health outcomes which are partially attributable to their exposure to marginalizing and interconnecting social issues like racism, sexism, economic inequality, and more. Although these groups of women have higher rates of SLE and though it is well known that they are at risk of exposure to marginalizing social phenomena, relatively little SLE literature explicitly links and addresses the relationship between marginalizing social issues and poor SLE-health outcomes among these women. Therefore, we developed a community-engaged partnership with two childhood-SLE diagnosed women of color to identify their perspectives on which systemic issues impacted on their SLE health-related outcomes. Afterward, we used Cochrane guidelines to conduct a rapid review associated with these identified issues and original SLE research. Then, we adapted an ecological model to illustrate the connection between systems issues and SLE health outcomes. Finally, we provided recommendations for ways to research and clinically mitigate SLE health inequities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".