Effectiveness of A Web-Based Course on Vaccination Competence in Higher Education: The Eduvac Erasmus+ Project
Bibliographic record
Abstract
Immunization is a highly cost-effective investment in health, proven to be an effective tool in controlling and eliminating dangerous infectious diseases. Health science students require evidence-based knowledge to tackle challenges in healthcare, particularly in the field of vaccination. The aim of the current study is to asses students’ knowledge on vaccinations and further explore their feedback after attending Educating Vaccination Competence web-based course (EDUVAC web-based course). Students from five Higher Educational Institutes voluntarily participated in the EDUVAC web-based course. The course provided various study materials, including PowerPoint presentations, videos, quizzes, texts, and references to reputable websites. It also offered small assignments and self-tests for self-evaluation. An online questionnaire was available to students before and after they completed the EDUVAC web-based course. The mean knowledge score on vaccines increased significantly after the EDUVAC web-based course (p<0.001). The majority of the students (95%) felt that the web-based course has benefitted them for their future career and 96.4% would encourage other students to attend the EDUVAC web-based course. Overall, our findings suggest that EDUVAC is a valuable resource for those seeking to enhance their understanding of vaccination.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".