Assessing the Functional Accessibility, Actionability, and Quality of Patient Education Materials from Canadian Cancer Agencies
Bibliographic record
Abstract
Patient education materials (PEM)s were extracted from provincial cancer agencies to determine their organizational health literacy by evaluating the quality, actionability, and functional accessibility (e.g., readability and understandability) of their PEMs. PEMs from 10 provincial agencies were assessed for their grade reading level (GRL), using eight numerical and two graphical readability scales, and underwent a difficult word analysis. The agencies were assessed for PEM quality using two methods (JAMA benchmarks and DISCERN), while actionability and understandability were assessed using the Patient Education Materials Assessment Tool (PEMAT). Seven hundred and eighty-six PEMs were analyzed. The overall average GRL was 9.3 ± 2.1, which is above the recommended 7th GRL for health information. The difficult word analysis showed that 15.4% ± 5.1% of texts contained complex words, 35.8% ± 6.8% of texts contained long words, and 24.2% ± 6.6% of texts contained unfamiliar words. Additionally, there was high overlap between the most frequently identified difficult words in the PEMs and the most frequently misunderstood words by cancer patients identified in the literature. Regarding quality indicators, no agency displayed all four indicators according to the JAMA benchmarks and DISCERN scores ranged between 38 (poor) to 66 (excellent). PEMAT scores ranged between 68% to 88% for understandability and 57% to 88% for actionability. PEMs continue to be written at a level above the recommended GRL across all provinces, and there was overall high variability in the quality, understandability, and actionability of PEMs among provincial agencies. This represents an opportunity to optimize materials, thus ensuring understanding by a wider audience and improving health literacy among Canadian cancer patients.
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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".