Editorial: eHealth literacy: Emergence of a new concept for creating, evaluating and understanding online health resources for the public
Bibliographic record
Abstract
The ability of consumers of health information to effectively understand, process and apply health information presented to them is a critical factor in improving health knowledge and developing effective health promotion strategies. Nowhere has this become more apparent than in efforts to apply information technology in the development of a range of systems and applications targeted for use by patients, and the general population. Indeed, success and failure of eHealth initiatives has been shown to depend on consideration of how to effectively design and deploy health information to consumers. Health literacy has become an important area of study that focuses on studying how health information can be understood and applied to improve health. In recent years the concept of eHealth literacy has also emerged, that sits at the intersection of health literacy and information technology literacy. In this special issue, a range of papers are presented that focus on the emerging concept of eHealth literacy. The papers in the special issue focus on basic definitional and conceptual issues as well as methodological approaches to studying health and eHealth literacy. A special focus of the issue is on how these concepts apply and can be adapted for improving health information technologies and applications.
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 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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".