Applying Website Rankings to Digital Health Centers in the United States to Assess Public Engagement: Website Usability Study
Bibliographic record
Abstract
BACKGROUND: As the public increasingly uses the internet to search for resources and information regarding health and medicine, it is important that health care organizations provide adequate web resources. Website usability refers to the ease of user experience on a website. In this study, we conducted usability analyses on digital health center websites. OBJECTIVE: The primary aims of this study were to (1) replicate a preexisting usability scoring methodology for digital health centers; (2) apply and test this replicated usability scoring methodology on a sample set of digital health center websites; and (3) derive recommendations from the results on potential areas of improvements for our sample of digital health center websites. METHODS: Website usability testing was conducted from March 1, 2020, to March 15, 2020. We replicated a methodology and scoring system from previous literature and applied them to digital health center websites. Our sample included 67 digital health centers that were affiliated with US universities or hospital systems. Usability was split into the following four broad categories: accessibility, marketing, content quality, and technology. Usability tools were used to score websites in each of the four categories. The composite of the key factors of each category was used to generate a general usability and overall usability score for each website. RESULTS: The category with the highest average score (6.3) was content quality. The content quality score also had the highest SD (2.18) and an SE of 0.27. The lowest performing category was technology, which had an average score of 0.9. The technology score also had the smallest SD (0.07) and an SE of 0.01. CONCLUSIONS: Our data suggest that content quality, on average, was the highest scoring variable among digital health center websites. As content is crucial to digital health knowledge, it is justified that digital health centers invest more resources into creating quality content. The overall lowest scoring variable was technology. Potential reasons for this finding include designated funding for servers, a lack of regulatory frameworks for social media presence and liability, and infrequent website audits. An easy approach for improving this variable is increasing website speed. Accessibility is another area that organizations can potentially improve. We recommend that these organizations perform periodic audits of their web presence with usability tools.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".