Association of Health Literacy and Numeracy With Lupus Knowledge and the Creation of the Lupus Knowledge Assessment Test
Bibliographic record
Abstract
OBJECTIVE: Limited health literacy and numeracy are associated with worse patient-reported outcomes and higher disease activity in systemic lupus erythematosus (SLE), but which factors may mediate this association is unknown. We sought to determine the association of health literacy and numeracy with SLE knowledge. METHODS: Patients with SLE were recruited from an academic center clinic. Participants completed validated assessments of health literacy (Newest Vital Sign [NVS]; n = 96) and numeracy (Numeracy Understanding in Medicine Instrument, Short Version [S-NUMI]; n = 85). They also completed the Lupus Knowledge Assessment Test (LKAT), which consists of 4 questions assessing SLE knowledge that were determined through consensus expert opinion for their wide applicability and importance related to self-management of the disease. Descriptive statistics and multivariable logistic regression modeling were used to analyze the results. RESULTS: In our SLE cohort (n = 125), 33% (32/96) had limited health literacy and 76% (65/85) had limited numeracy. The majority correctly identified that hydroxychloroquine prevented SLE flares (91%); however, only 23% of participants correctly answered a numeracy question assessing which urine protein to creatinine (UPC) ratio was > 1000 mg/g. The mean LKAT score was 2.7 out of 4.0. Limited health literacy, but not numeracy, was associated with lower knowledge about SLE as measured by the LKAT, even after adjusting for education. CONCLUSION: Patients with SLE with limited health literacy had lower knowledge about SLE. The LKAT could be further refined and/or used as a screening tool to identify patients with knowledge gaps. Further work is needed to improve patients' understanding of proteinuria and investigate whether literacy-sensitive education can improve care.
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".