Knowledge Regarding Hepatitis B and C Infections among Health Science Students at Taibah University
Bibliographic record
Abstract
OBJECTIVES: Health science students are at a high risk of contracting hepatitis B virus (HBV) and hepatitis C virus (HCV) due to the likelihood of accidental exposure to contaminated blood. This study aimed to determine the level of knowledge regarding HBV and HCV among Taibah University health science students. METHODS: A cross-sectional survey was conducted among health science students from Taibah University, Saudi Arabia, using a validated online questionnaire from 14 February 2022 to 9 July 2022. HBV and HCV knowledge levels among applied medical sciences (AMS), nursing, medicine, medical rehabilitation sciences (MRS), pharmacy, and dentistry undergraduate students were evaluated. The questionnaire was divided into two parts. The first part comprised 5 demographic questions, while the second part comprised 10 questions regarding HBV and HCV infection. RESULTS: A total of 369 students participated in the survey. Knowledge levels regarding HBV and HCV were relatively low, with a mean knowledge score of 6.8 ± 1.8 (out of 10). Results revealed a positive correlation between students’ knowledge levels and year of education, with knowledge scores increasing with increases in participants’ academic year. Knowledge levels were primarily impacted by students’ disciplines. CONCLUSION: This study revealed inadequate levels of knowledge regarding HBV and HCV among health science students at Taibah University. There was a positive correlation between knowledge level and academic year. Efforts should be made to improve HBV and HCV knowledge through awareness campaigns, educational interventions, and preventive measure training.
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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 | 0.002 |
| 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 | 0.001 | 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".