Developing a Framework to Measure Health Literacy Demands of Consumer-Facing Healthcare Organization Websites
Bibliographic record
Abstract
Many consumer-facing healthcare organization websites are challenging for people with low e-health literacy skills to navigate and use the information to make informed decisions. Though searchers may be familiar with how to use the Internet, navigating website designs, often referred to as eHealth literacy, can make finding health information confusing. Health literacy demands, related to eHealth literacy skills concerning the content and design, can make the difference between consumers’ frustration and success. In this study, we aimed to create a framework to measure the accessibility and usability of healthcare organization consumer-facing websites using this question, “How can the information offered, and navigation of consumer-facing healthcare organization websites be improved to increase accessibility and usability?” We scored select healthcare organizations’ consumer-facing websites including their home page and two patient education pages, using Social Cognitive Theory and Health Literacy constructs both of which promote better accessibility and usability of health information. In addition, we analyzed how the health literacy demands of these pages support or obstruct the eHealth literacy skills of consumers. Results indicated that only 50% of Social Cognitive Theory constructs and 47% of Health Literacy constructs were observed. However, by examining the missing constructs healthcare organizations can assess where to focus consumer-facing website improvement efforts. Therefore, we concluded that Social Cognitive Theory combined with Health Literacy constructs offer a viable framework for measuring and potentially improving consumer-facing healthcare websites.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".