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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".