COVID-19 Vaccination and Predictive Factors in Immigrants to Europe: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Vaccination plays a pivotal role in the control of infectious disease outbreaks. Hesitancy/refusal of the vaccine by immigrants poses a serious threat to their and society's health. We reviewed studies regarding COVID-19 vaccine uptake in Europe by first-generation immigrants. A systematic review (PROSPERO: CRD42023432142), conducted until 31 October 2023 using Web of Science, PubMed, and Scopus, identified 295 potential articles. Of these, 16 conducted on 2,009,820 immigrants in nine European countries met the eligibility criteria. Most studies were of medium/high quality according to the Newcastle-Ottawa Scale adapted for observational studies. Factors that affected the uptake or hesitancy/refusal to vaccinate, with particular regard to gender, age, and country of origin, were examined. The meta-analysis of eight studies revealed that the pooled estimated prevalence of COVID-19 vaccine uptake in first-generation immigrants was 71.3% (95% CI: 70.0-72.5%), corresponding to 13.3% less than the host country population (95% CI: 10.2-16.4%). Limitations of included studies and this review were deeply discussed, highlighting the need for further research on the effect of acculturation on second-generation immigrants. European governments need to ensure equal availability of COVID-19 and other health-saving vaccines to all immigrants in the future by overcoming cultural barriers, building trust in institutions, and improving communication.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".