eHealth literacy issues, constructs, models, and methods for health information technology design and evaluation
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.
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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 | 0.143 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".