Evaluating the Readability and Understandability of Online Patient Educational Material for Percutaneous Coronary Intervention in Canada
Bibliographic record
Abstract
Background: Percutaneous coronary intervention (PCI) is the most common treatment for coronary artery disease revascularization. Many patients undergoing PCI may seek educational information online, but the reliability of such resources remains uncertain. This study seeks to assess the readability and understandability of online patient resources for PCI from Canadian hospital sources. Methods: We performed a descriptive study evaluating online educational materials pertaining to PCI hosted by all Canadian hospitals that perform the procedure. The primary outcomes were readability, assessed using the Flesch-Kincaid Grade Level (FKGL) and Scolarius score, and understandability plus actionability, as assessed using the Patient Education Materials Assessment Tool (PEMAT). Educational clinical material is recommended to be written at an FKGL between 6 and 8. A score between 50 and 89 on the Scolarius tool suggests the text is readable by most adults, and a PEMAT score >70% corresponds to an understandable and actionable educational material. Results: A total of 29 Canadian hospitals performing PCI and hosting unique educational content were identified. Only 71% of PCI-capable hospitals provide relevant online educational resources to patients. The average FKGL of the analyzed content was 10 (range 5-18) and the average Scolarius score was 127.8 (range 79-173). The average total PEMAT print score was 46.1%, whereas the average total PEMAT audiovisual score was 71.8%. Conclusions: Most of the educational material pertaining to PCI created by Canadian hospitals is in English and print format, and of poor readability, understandability, and actionability. Audiovisual materials perform better but are sparsely used.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".