Perceived and Performed eHealth Literacy: Survey and Simulated Performance Test
Bibliographic record
Abstract
BACKGROUND: Electronic health (eHealth) literacy of consumers is essential in order to improve information and communication technology (ICT) use for health purposes by ordinary citizens. However, performed eHealth literacy is seldom studied. Therefore, the present study assessed perceived and performed eHealth literacy using the recent conceptualization of health literacy skills. OBJECTIVE: The aim of this paper was to examine the association between perceived and performed eHealth literacies. METHODS: In total, 82 Israeli adults participated in the study, all 50 years and older, with a mean age of 67 (SD 11). Of the participants, 60% (49/82) were women and 72% (59/82) had a post-secondary education. The participants were first surveyed and then tested in a computer simulation of health-related Internet tasks. Performed, perceived (eHealth Literacy Scale, eHEALS), and evaluated eHealth literacy were assessed, and performed eHealth literacy was also recorded and re-evaluated later. Performance was scored for successful completion of tasks, and was also assessed by two researchers for motivation, confidence, and amount of help provided. RESULTS: The skills of accessing, understanding, appraising, applying, and generating new information had decreasing successful completion rates. Generating new information was least correlated with other skills. Perceived and performed eHealth literacies were moderately correlated (r=.34, P=.01) while facets of performance (ie, digital literacy and eHealth literacy) were highly correlated (r=.82, P<.001). Participants low and high in performed eHealth literacy were significantly different: low performers were older and had used the Internet for less time, required more assistance, and were less confident in their conduct than high performers. CONCLUSIONS: The moderate association between perceived and performed eHealth literacy indicates that the latter should be assessed separately. In as much, the assessment of performed eHealth literacy in clinical settings should entail the structuring of tasks as well as shortening and automatizing the assessment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".