Differences in the Level of Electronic Health Literacy Between Users and Nonusers of Digital Health Services: An Exploratory Survey of a Group of Medical Outpatients
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Digitalization of health services ensures greater availability of services and improved contact to health professionals. To ensure high user adoption rates, we need to understand the indicators of use and nonuse. Traditionally, these have included classic sociodemographic variables such as age, sex, and educational level. Electronic health literacy (eHL) describes knowledge, skills, and experiences in the interaction with digital health services and technology. With our recent introduction of 2 new multidimensional instruments to measure eHL, the eHL questionnaire (eHLQ) and the eHL assessment (eHLA) toolkit, eHL provides a multifaceted approach to understand use and nonuse of digital health solutions in detail. OBJECTIVE: The aim of this study was to investigate how users and nonusers of digital services differ with respect to eHL, in a group of patients with regular contact to a hospital outpatient clinic. Furthermore, to examine how usage and nonusage, and eHL levels are associated with factors such as age, sex, educational level, and self-rated health. METHODS: Outpatients were asked to fill out a survey comprising items about usage of digital services, including digital contact to general practitioner (GP) and communication via the national health portal sundhed.dk, the eHLQ, and the eHLA toolkit, as well as items on age, sex, education, and self-rated health. In total, 246 patients completed the survey. A Mann-Whitney test was used to test for differences between users and nonusers of digital services. Correlation tests described correlations between eHL scales (eHEALSs) and age, education, and self-rated health. A significance level of .0071 was used to reject the null hypothesis in relation to the eHEALSs and usage of digital services. RESULTS: In total, 95.1% (234/246) of the participants used their personal digital ID (NemID), 57.7% (142/246) were in contact with their GPs electronically, and 54.0% (133/246) had used the national health portal (sundhed.dk) within the last 3 months. There were no differences between users and nonusers of sundhed.dk with respect to age, sex, educational level, and self-rated health. Users of NemID scored higher than nonusers in 6 of the 7 dimensions of eHLQ, the only one which did not differ was dimension 2: Understanding of health concepts and language. Sundhed.dk users had a higher score in all of the 7 dimensions except for dimension 4: Feel safe and in control. The eHLA toolkit showed that users of sundhed.dk and NemID had higher levels of eHL with regard to tools 2, 5, 6, and 7. Furthermore, users of sundhed.dk had higher levels of eHL with regard to tools 3 and 4. CONCLUSIONS: Information about patients' eHL may provide clinicians an understanding of patients' reasons for not using digital health services, better than sociodemographic data or self-rated health.
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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 | 0.054 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it