An analysis of the readability of patient information materials for common urological conditions
Bibliographic record
Abstract
INTRODUCTION: Health literacy has been shown to be an important determinant of outcomes in numerous disease states. In an effort to improve health literacy, the Canadian Urological Association (CUA) publishes freely accessible patient information materials (PIMs) on common urological conditions. We sought to evaluate the readability of the CUA's PIMs. METHODS: All PIMs were accessed through the CUA website. The Flesch Reading Ease Score (FRES), the Flesch-Kincaid Grade Level (FKGL), and the number of educational graphics were determined for each PIM. Low FRES scores and high FKGL scores are associated with more difficult-to-read text. Average readability values were calculated for each PIM category based on the CUA-defined subject categorizes. The five pamphlets with the highest FKGL scores were revised using word substitutions for complex multisyllabic words and reanalyzed. The Kruskal-Wallis test was used to identify readability differences between PIM categories and paired t-tests were used to test differences between FKGL scores before and after revisions. RESULTS: Across all PIMs, FRES values were low (mean 47.5, standard deviation [SD] 7.47). This corresponded to an average FKGL of 10.5 (range 8.1-12.0). Among PIM categories, the infertility and sexual function PIMs exhibited the highest average FKGL (mean 11.6), however, differences in scores between categories were not statistically significant (p=0.38). The average number of words per sentence was also highest in the infertility and sexual function PIMs and significantly higher than other categories (mean 17.2; p=0.01). On average, there were 1.4 graphics displayed per PIM (range 0-4), which did not vary significantly by disease state (p=0.928). Simple words substitutions improved the readability of the five most difficult-to-read PIMs by an average of 3.1 grade points (p<0.01). CONCLUSIONS: Current patient information materials published by the CUA compare favourably to those produced by other organizations, but may be difficult to read for low-literacy patients. Readability levels must be balanced against the required informational needs of patients, which may be intrinsically complex.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".