Scoping Review on Barriers and Challenges to Pediatric Immunization Uptake among Migrants: Health Inequalities in Italy, 2003 to Mid-2023
Bibliographic record
Abstract
In the aftermath of the COVID-19 pandemic, asylum seekers, refugees, and foreign-born migrants are more likely to suffer from physical, mental, and socioeconomic consequences owing to their existing vulnerabilities and worsening conditions in refugee camps around the world. In this scenario, the education of migrants and newcomers about immunization is critical to achieving health equity worldwide. Globally, it is unclear whether government vaccination policies are prioritizing the health information needs of migrants. We searched for studies investigating the vaccination uptake of migrant children settled in Italy that were published between January 2003 and 25 June 2023. Following Arksey and O'Malley's five-stage method for scoping reviews, all potentially relevant literature published in English was retrieved from SciSearch, Medline, and Embase. This search resulted in 88 research articles, 25 of which met our inclusion criteria. Our findings indicate unequal access to vaccination due to a lack of available information in the native language of the immigrants' country of origin, vaccine safety concerns or lack of awareness, logistical difficulties, and fear of legal consequences. The findings strongly encourage further government and political discourse to ensure migrants have fair, equitable, ethical, and timely access to essential medicines.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".