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Record W4386168241 · doi:10.3390/vaccines11091417

Scoping Review on Barriers and Challenges to Pediatric Immunization Uptake among Migrants: Health Inequalities in Italy, 2003 to Mid-2023

2023· review· en· W4386168241 on OpenAlexaff
Samina Sana, Elisa Fabbro, Andrea Zovi, Antonio Vitiello, Toluwani Ola-Ajayi, Ziad Zahoui, Bukola Salami, Michela Sabbatucci

Bibliographic record

VenueVaccines · 2023
Typereview
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsInequalityImmunizationEnvironmental healthRoutine immunizationMedicineImmunology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.402
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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