Vaccine hesitancy among hospital workers
Bibliographic record
Abstract
Introduction Vaccine hesitancy is a serious issue and it affects the scientific achievements of health. This phenomenon has begun to be studied more often in health care workers, to find its determining factors. Objectives The aim was to determine the percentage of hospital workers who got vaccinated against the infection with SARS-CoV-2. Methods Beginning with October 2021, we conducted an online questionnaire in which 57 hospital workers participated. Preliminary results allowed us to assess the rate of vaccine hesitancy among this group. Results Out of the 57 hospital workers, the majority were vaccinated (n=45, 78.94%) in comparison to less than a quarter (n=12, 21.05%) that refused vaccination. The group of hospital workers included mostly nurses ( n=21, 36.84%). Also, 12 psychologists (21.05%), 11 doctors (19.29%), and 10 students (17.54%) were included. Among the cases that did not accept getting vaccinated against COVID-19, the highest percentage was occupied by nurses (n=9, 15.78%). Moreover, there were only one doctor and one psychologist who did not get vaccinated. Conclusions In the current pandemic times, the hesitancy and refusal of vaccination prove to be very challenging. It is important to explore their reasons and to promote health education programs. Disclosure No significant relationships.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".