Determinants of COVID-19 vaccination acceptance or hesitancy in Italy: an overview of the current evidence.
Bibliographic record
Abstract
INTRODUCTION: Vaccine hesitancy is a major public health issue and a challenge for the implementation of COVID-19 immunization campaigns. The objective of this study was to address the determinants of COVID-19 vaccination acceptance or hesitancy in the Italian population. MATERIALS AND METHODS: We conducted a rapid systematic review by searching PubMed until May 3rd, 2022, according to Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. Articles assessing determinants of Italians' attitudes towards COVID-19 vaccination in terms of hesitancy and/or acceptance were considered eligible. Quality and risk of bias assessment was performed through the Newcastle Ottawa Scale appraisal tool. Determinants were grouped in three categories: contextual, individual and group, and vaccine/vaccination specific influences. RESULTS: Out of 606 articles, 59 studies were included in the analysis. Included studies demonstrated that, in Italy, COVID-19 vaccination acceptance or hesitancy is mostly influenced by perceived safety, efficacy and usefulness of the vaccine. CONCLUSION: These findings should be considered to plan tailored interventions for counteracting COVID-19 vaccination hesitancy in Italy.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".