Nursing students and COVID-19 vaccination. ESitA Study An Observational Study.
Bibliographic record
Abstract
BACKGROUND AND AIM: Vaccine hesitancy is an important problem in terms of health policy. This historical moment leads us to wonder if vaccine hesitancy is also present among nursing students who should be particularly sensitive to the subject Methods: Between February 10 to February 17 2021, 1080 students enrolled in the Bachelor of Science in Nursing in the Department of Medicine and Surgery of the University of Perugia, were invited to reply to an online questionnaire sent to their university email accounts. RESULTS AND CONCLUSIONS: A certain amount of vaccination hesitancy was detected among the students in our study. It can be assumed that the issues surrounding the AstraZeneca vaccine, which occurred at the start of the vaccination campaign, may have led to an increase in people's hesitancy. Boosting vaccination campaigns, including appropriate use of social media, may lead to greater acceptance. Also, it would be useful to assess the cultural basis of the recent anti-Vax controversy, particularly for students of nursing or other health professions, who should be able to evaluate, source and recognize the most validated data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".