Health Literacy and Preventive Behaviors of Undergraduate University Students During the COVID-19 Pandemic
Bibliographic record
Abstract
The situation of the coronavirus (COVID-19) pandemic is full of unpredictability, uncertainty about the severity of the disease, and incorrect information. Therefore, health literacy preparation is the key to preventing COVID-19 and having the correct health behaviors. The objectives of this study were 1) to study health literacy on COVID-19 and prevention behaviors of COVID-19 among undergraduate students at Mahasarakham University, and 2) to compare health literacy on COVID-19 and prevention behaviors of COVID-19 among undergraduate students at Mahasarakham University, classified by genders, academic years, grade point averages (GPAs), and faculty groups. The participants were 417 undergraduate students at Mahasarakham University chosen by stratified random sampling and simple random sampling. The research instruments were as follows: the questionnaire on health literacy on COVID-19 and the questionnaire on COVID-19 prevention’s behavior. The data were analyzed using percentage (%), mean (M), standard deviation (S.D.), independent sample t-test, one-way ANOVA. The findings revealed that 1) undergraduate students were well versed in health literacy for the COVID-19 infection and their prevention behaviors of COVID-19 infection were at a good level (M = 90.06, S.D. = 9.54; M = 86.87, S.D. = 11.50) and 2) female undergraduate students had statistically higher mean scores on COVID-19 health literacy scores and COVID-19 prevention’s behaviors than males. Students from the health sciences faculty group had statistically significantly higher average health literacy scores on COVID-19 infection than those from the technology sciences faculty group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".