Attitudes towards immunization and the state of knowledge about vaccines among Polish students
Bibliographic record
Abstract
Introduction: Although vaccines are said to be one of the most significant discoveries in contemporary medicine, the problem of vaccine hesitancy is becoming an increasingly important public health issue.Such a state of affairs poses a significant risk of rare disease outbreaks.Moreover, the recent COVID-19 pandemic was brought under control, among other things, by mass vaccinations -yet still, many people are reluctant to use them.This research assesses possible reasons behind the problem and its magnitude in a group of Polish students from various universities.Material and methods: A cross-sectional study was conducted in April-June 2021 with a self-administered questionnaire among 301 undergraduate students from Polish universities.Results: Students' trust in the vaccines' effectiveness mainly depended on the field of studies, the kind of sources of knowledge about vaccines they used, their knowledge levels about them, and the experience of developing an illness that a vaccine should protect them against.The kind of sources of knowledge and the levels of knowledge affected students' decisions about whether they wanted to vaccinate their future children or not.Respondents' levels of knowledge about vaccines correlated with their subject of studies and were the highest among those who used mostly scientific sources of knowledge about the vaccines.Conclusions: This research demonstrates that education improvement regarding the process of immunization is a crucial step towards the solution to the problem of vaccine hesitancy.It is also very significant to promote the use of verified scientific sources of information about vaccines.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".