The Effect of Health Literacy Level on the Use of E-Health Applications
Bibliographic record
Abstract
Purpose: The purpose of the study is to measure the effect of health literacy (HL) level on the level of use of e-health applications among public employees, excluding health workers serving directly to the public and working in public institutions in the downtown area of Yozgat, Turkey. Methods: The study is cross-sectional and was conducted in 2021 among public employees. 476 public personnel working in state institutions in the city center participated in the study. Chi-square test, t-test, ANOVA, and multinomial logistic regression were used to evaluate the data. Results: Of the participants, 64.3% of them were male, 74.9% were married, 45.3% were in the 30-39 age group, and 60.9% were undergraduates. It was observed that 21.5% of the people in the research group had insufficient health literacy (SSL), 41.3% were problematic and 37.2% were sufficient. It was seen that the most used E-health application was E-pulse with 84.9%, followed by Life Fits into Home (LFH) and Central Physician Appointment System (CPAS) (64.3%), and the lowest was the hospitals' online systems (29.1%). The use of E-Nabız (e-Pulse) and E-Devlet (e-Government) SSI applications according to HL level was not found to be statistically significant (p>0.05). Conclusion: The vast majority of public employees use E-Pulse, and approximately 2/3 of them use LFH and CPAS. Less than half of the participants in the study had a sufficient health-literacy level, and the effect on e-Health practices was not found significant.
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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 | 0.065 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".