eHealth Literacy: In the Quest of the Contributing Factors
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
BACKGROUND: Understanding the factors that influence eHealth in a country is particularly important for health policy decision makers and the health care market, as it provides critical information to develop targeted and tailored interventions for relevant patient-consumer segments, and further suggests appropriate strategies for training the health illiterate part of the population. OBJECTIVE: The objective of the study is to assess the eHealth literacy level of Greek citizens, using the eHealth Literacy Scale (eHEALS), and further explore the factors that shape it and are associated with it. METHODS: This empirical study relies on a unique sample of 1064 citizens in Greece in the year 2013. The participants were requested to answer various questions about their ability to solve health-related issues using the Internet, and to provide information about their demographic characteristics and life-style habits. Ordered logit models were used to describe a certain citizen's likelihood of being eHealth literate. RESULTS: The demographic factors show that the probability of an individual being eHealth literate decreases by 23% (P=.001) when the individual ages and increases by 53% (P<.001) when he or she acquires higher level of education. Among the life-style variables, physical exercise appears to be strongly and positively associated with the level of eHealth literacy (P=.001). Additionally, other types of technology literacies, such as computer literacy and information literacy, further enhance the eHealth performance of citizens and have the greatest impact among all factors. CONCLUSIONS: The factors influencing eHealth literacy are complex and interdependent. However, the Internet is a disruptive factor in the relationship between health provider and health consumer. Further research is needed to examine how several factors associate with eHealth literacy, since, the latter is not only related to health care outcomes but also can be a tool for disseminating social inequalities.
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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 | 0.058 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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