Health Literacy Predicts Discrepancies Between Traditional Written Patient Assessments and Verbally Administered Assessments in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Patient assessments of disease activity (PtGA) and general health (GH) measured by visual analog scale (VAS) are widely used in rheumatoid arthritis (RA) clinical practice and research. These require comprehension of the question's wording and translation of disease activity onto a written VAS, which is problematic for patients with limited health literacy (HL) or difficulty completing forms. This study's objective was to validate verbally administered versions of patient assessments and identify factors that might explain discrepancies between verbal and written measures. METHODS: We enrolled patients with RA at the Denver Health rheumatology clinic (n = 300). Subjects were randomized to complete the traditional written PtGA and GH and one of the verbal assessments. Subjects provided a verbal numeric response after reading the question, having the question read to them in person, or hearing the question over the phone. Spearman and Lin correlations comparing written and verbal assessments were determined. Multivariate logistic regression was performed to explain any discrepancies. RESULTS: The instruments administered verbally in-person showed good, but not excellent, correlation with traditional written VAS forms (Spearman coefficients 0.59 to 0.70; p < 0.001 for all correlations). Twenty-three percent of subjects were unable to complete 1 of the written VAS assessments without assistance. HL predicted missing written data and discrepancies between verbal and written assessments (p < 0.05 for all correlations). CONCLUSION: Providers should use verbal versions of PtGA and GH with caution while caring for patients unable to complete traditional written version. Limited HL is widely prevalent and a barrier to obtaining patient-oriented data.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".