Attitudes of the Population Toward Vaccines During the COVID‐19 Pandemic: The PROACTIVE Study
Bibliographic record
Abstract
BACKGROUND: Vaccination is a vital strategy to prevent infectious diseases and reduce mortality globally. However, vaccine hesitancy threatens these efforts, especially highlighted during the COVID-19 pandemic. Understanding factors influencing vaccination decisions is crucial for improving public health strategies. OBJECTIVE(S): To investigate the attitudes of the Italian general population toward mandatory (e.g., HBV or tetanus) or recommended (e.g., influenza, HPV, or meningococcus) vaccinations, factors influencing vaccine uptake, and risk perceptions related to COVID-19. DESIGN: A cross-sectional descriptive study using the PROACTIVE Survey questionnaire. SAMPLE: The study included 411 participants aged 18-98 years from the general Italian population, recruited via convenience and snowball sampling in June 2022. MEASUREMENTS: Data included sociodemographic characteristics, adherence to vaccinations, COVID-19 experiences, preventive behaviors, and individual risk perceptions. Inferential statistics included Pearson's r correlation, t-test, and analysis of variance (ANOVA) to explore correlations and differences. RESULTS: Adherence to preventive measures positively correlated with risk perceptions (r = 0.358, p < 0.001). Females, older individuals, and those with chronic conditions showed higher adherence to preventive behaviors. Previous adherence to vaccines correlated with greater COVID-19 preventive behaviors (r = 0.124, p = 0.012). CONCLUSIONS: Age, gender, risk perceptions, and chronic conditions significantly influenced vaccination attitudes and preventive measures. These findings underscore the need for tailored public health strategies, especially in post-pandemic contexts, to address vaccine hesitancy and improve vaccination campaigns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".