Perceived Transactional eHealth Literacy and Its Association with the Demographic Characteristics among Undergraduate Level Students of Dhaka City in Bangladesh
Bibliographic record
Abstract
Background: The number of internet user for health information is increasing day by day. eHealth literacycan promote knowledge and engagement, which will increase the frequency of information-seeking, effectivepatient-healthcare provider communication, proactive health behaviors, and better health related quality of lifethrough effective use of internet. Objective: To assess eHealth literacy and perceived trust on digital channelsfor available health information among the undergraduate level students of Dhaka City, Bangladesh. Methods:This cross-sectional study was conducted, between January and December of 2023, among the undergraduatestudents hailing from four institutions of Dhaka city namely Jagannath University, Daffodil InternationalUniversity, Enam Medical College and Mandy Dental College. A total of 384 students participated in thisstudy. pretested questionnaire including Transactional eHealth Literacy Instrument was administered for datacollection. Results: Among 384 students, most of them were ≥21 years. Male students were predominant.Most of the respondents used internet for health information (55.5%) and preferred digital channels (89%)over broadcast channels for health information. The mean score of eHealth literacy was 3.56. Translationalliteracy was highest among the four categories (mean=3.80). The scores for communicative, critical, andfunctional literacy were 3.34, 3.44, and 3.73, in that order. Thus, communicative literacy is the area with thelowest literacy for the respondents. Significantly higher eHealth literacy was found among ≥21 years students,and students of medical background, used intenet within the last week and digital channels users for healthinformation (P>0.05). However, no difference was found between male and female students. Conclusion:Our data revealed that eHealth literacy among the students was moderate. Improvement of eHealth literacy isneeded for mass health promotion in the country.International Journal of Human and Health Sciences Vol. 08 No. 04 Oct’24 Page: 321-327
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".