Applying Usability Methods to Identify Health Literacy Issues: An Example Using a Personal Health Record
Bibliographic record
Abstract
The prevalence of consumer health information systems is increasing. However, usability and health literacy impact both the value and adoption of these systems. Health literacy and usability are closely related in that systems may not be used accurately if users cannot understand the information therein. Thus, it is imperative to focus on mitigating the demands on health literacy in consumer health information systems. This study modified two usability evaluation methods (heuristic evaluation and usability testing) to incorporate the identification of potential health literacy issues in a Personal Health Record (PHR). Heuristic evaluation is an analysis of a system performed by a usability specialist who evaluates how well the system abides by usability principles. In contrast, a usability test involves a post hoc analysis of a representative user interacting with the system. These two methods revealed several health literacy issues and suggestions to ameliorate them were made. Thus, it was demonstrated that usability methods could be successfully augmented for the purpose of investigating health literacy issues. To improve users' health knowledge, the adoption of consumer health information systems, and the accuracy of the information contained therein, it is encouraged that usability methods be applied with an added focus on health literacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".