Evaluation of Online Written Medication Educational Resources for People Living With Heart Failure
Bibliographic record
Abstract
Background: Patient educational resources on heart failure (HF) medications may improve patient understanding, which is critical for informed decision-making and patient self-efficacy. The purpose of our study was to evaluate the quality and readability of written medication educational resources available online. Methods: Two investigators searched Google, Yahoo, and Bing for written patient educational resources that addressed at least one HF medication. We assessed educational quality using the Ensuring Quality Information for Patients (EQIP) tool (range 0 [worst] to 100 [best]), and we evaluated readability using the Flesch-Kincaid Grade Level. Results: From 693 identified webpages, 39 HF medication educational resources met study eligibility. Among included resources, the median Ensuring Quality Information for Patients score was 61% (interquartile range 54%-68%), with 2 (5%) rated as high quality (score ≥ 75%). The median Flesch-Kincaid Grade Level was 8 (interquartile range 8-12), with 4 (10%) resources meeting the recommended 6th-grade reading level. Conclusions: Most HF medication educational resources available on the Internet are of acceptable educational quality, but could readily be improved. Most resources were beyond the recommended reading grade level for educational resources, limiting their utility for patients with a low literacy level.
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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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".