Meta-analysis: eHealth literacy and attitudes towards internet/computer technology
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between eHealth literacy and attitudes towards internet/computer (I/C) technology use in healthcare. METHODS: Analysis of data from 16 cross-sectional studies, involving literature search from databases like PubMed, EBSCO, JMIR, up to April 2023. Studies were selected based on a quantitative cross-sectional design, with no restrictions on participant characteristics. RESULTS: A significant positive correlation (0.36; 95% CI 0.37-0.38, p < 0.05) was found between eHealth literacy and positive attitudes towards I/C technology use. Age and regional differences, especially in participants over 50 and from Asian and Middle Eastern countries, were notable. CONCLUSION: Lower eHealth literacy is associated with more negative attitudes towards I/C technology in healthcare. This trend is consistent across diverse demographics and regions.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".