A Tale of Two Inspection Methods: Comparing an eHealth Literacy and User Experience Checklist with Heuristic Evaluation
Bibliographic record
Abstract
Adhering to user experience (UX) and eHealth literacy principles when developing consumer health information systems (HISs) can not only improve a user's experience but can also have implications on patient safety. Methods exist to explore these dimensions independently, but few methods are available for evaluating consumer (i.e., citizen) health information systems for their adherence to usability and eHealth literacy design principles simultaneously. In this paper, we compared two inspection (i.e., expert review) tools and identified the strengths and weaknesses of each. The findings from this comparison can assist researchers, consumer health information system developers, and evaluators choosing between the two alternatives. Moreover, our comparison revealed the shortcomings in both tools and the need for a novel, purpose-built tool that is more comprehensive than either of the existing tools that assess UX and eHealth literacy and more adequately address design guidelines for the mobile environment.
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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 | 0.149 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".