Assessing the readability and patient comprehension of rheumatology medicine information sheets: a cross-sectional Health Literacy Study
Bibliographic record
Abstract
OBJECTIVES: Patients are often provided with medicine information sheets (MIS). However, up to 60% of patients have low health literacy. The recommended readability level for health-related information is ≤grade 8. We sought to assess the readability of MIS given to patients by rheumatologists in Australia, the UK and Canada and to examine Australian patient comprehension of these documents. DESIGN: Cross-sectional study. SETTING: Community-based regional rheumatology practice. PARTICIPANTS: Random sample of patients attending the rheumatology practice. OUTCOME MEASURES: Readability of MIS was assessed using readability formulae (Flesch Reading Ease formula, Simple Measure of Gobbledygook scale, FORCAST (named after the authors FORd, CAylor, STicht) and the Gunning Fog scale). Literal comprehension was assessed by asking patients to read various Australian MIS and immediately answer five simple multiple choice questions about the MIS. RESULTS: The mean (±SD) grade level for the MIS from Australia, the UK and Canada was 11.6±0.1, 11.8±0.1 and 9.7±0.1 respectively. The Flesch Reading Ease score for the Australian (50.8±0.6) and UK (48.5±1.5) MIS classified the documents as 'fairly difficult' to 'difficult'. The Canadian MIS (66.1±1.0) were classified as 'standard'. The five questions assessing comprehension were correctly answered by 9/21 patients for the adalimumab MIS, 7/11 for the methotrexate MIS, 6/28 for the non-steroidal anti-inflammatory MIS, 10/11 for the prednisone MIS and 13/24 for the abatacept MIS. CONCLUSIONS: The readability of MIS used by rheumatologists in Australia, the UK and Canada exceeds grade 8 level. This may explain why patient literal comprehension of these documents may be poor. Simpler, shorter MIS with pictures and infographics may improve patient comprehension. This may lead to improved medication adherence and better health outcomes.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".