Assessment of the Readability of Online Patient Education Material from Major Geriatric Associations
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: An increasing number of patients are using the internet to supplement information provided by medical professionals. Online geriatric patient education materials (PEMs) should be written at or below a 6th grade reading level (GRL) that takes into account barriers unique to the geriatric population. The objectives of the study are to assess PEMs of geriatric associations' websites and determine whether they are above the GRL recommended by the Centers for Disease Control and National Institutes of Health. DESIGN: Descriptive and correlational methodology. PEMs from 10 major geriatric associations were assessed for their GRL using 10 scales. Eight of the scales provide a numerical GRL while two of the scales provide a visual representation of the GRLs. Analysis was conducted using Readability Studio 2019.3. SETTING: Analysis was conducted February 2020. PARTICIPANTS: Identified 10 geriatric associations and 884 PEMs. MEASUREMENTS: GRLs were measured by 10 validated readability indices: the Degrees of Reading Power and Grade Equivalent test, Flesch-Kincaid grade level, Simple Measure of Gobbledygook test, Coleman-Liau Index, Gunning Fog Index, New Fog Count, New Dale-Chall readability formula, Ford, Caylor, Sticht scale, Raygor readability estimate graph, and Fry readability graph. RESULTS: The mean of all PEMs using the numerical scales was 11.1 ± 2.4. Ninety-nine percent of PEMs are above the 6th GRL. PEMs ranged from a grade 3.0 to 19.0 reading level. Analysis of variance demonstrated a significant difference between associations (P < .0001), and multiple comparison analysis identified the National Institute on Aging as the content easiest to read (9.5 ± 1.6). CONCLUSION: PEMs from geriatric association websites are written above the recommended 6th GRL. As patients increasingly look toward online supplementary health information during COVID-19, there is an opportunity for improving PEMs to enable greater comprehension by the target population.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".