eHealth literacy issues, constructs, models, and methods for health information technology design and evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The concept of eHealth literacy is beginning to be recognized as a being of key importance in the design and adoption of effective and efficient health information systems and applications targeted to lay people and patients. Indeed, many systems such as patient portals and personal health records have not been adopted due to a mismatch between the level of eHealth literacy demanded by a system and the level of eHealth literacy possessed by end users. The purpose of this paper is to present an overview of important concepts related to eHealth literacy, as well as how the notion of eHealth literacy can be applied to improve the design and adoption of consumer health information systems. This paper begins with describing the importance of eHealth literacy with respect to design of health applications for the general public paired with examples of consumer health information systems whose limited success and adoption has been attributed to the lack of consideration for eHealth literacy. This is followed by definitions of what eHealth literacy is and how it emerged from the related concept of health literacy. A model for conceptualizing the importance of aligning consumers’ eHealth literacy skills and the demands systems place on their skills is then described. Next, current tools for assessing consumers’ eHealth literacy levels are outlined, followed by an approach to systematically incorporating eHealth literacy in the deriving requirements for new systems is presented. Finally, a discussion of evolving approaches for incorporating eHealth literacy into usability engineering methods is presented.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it