Vaccine Hesitancy among Immigrants: A Narrative Review of Challenges, Opportunities, and Lessons Learned
Bibliographic record
Abstract
(1) Background: Vaccination reluctance is a major worldwide public health concern as it poses threats of disease outbreaks and strains on healthcare systems. While some studies have examined vaccine uptake within specific countries, few provide an overview of the barriers and trends among migrant groups. To fill this knowledge gap, this narrative review analyzes immunization patterns and vaccine hesitancy among immigrant populations. (2) Methods: Four researchers independently evaluated the quality and bias risk of the 18 identified articles using validated critical appraisal tools. (3) Results: Most studies focused on vaccine hesitancy among migrants in the United States and Canada, with a higher COVID-19 vaccine reluctance than native-born residents. Contributing factors to this hesitancy include demographics, cultural views, obstacles to healthcare access, financial hardship, and distrust in health policies. Additionally, immigrants in North America and Europe face unfair vaccine challenges due to misinformation, safety concerns, personal perspectives, language barriers, immigration status, and restricted healthcare access. (4) Conclusions: Tailored vaccine education programs and outreach campaigns sensitive to immigrants' diversity should be developed to address this issue. It is also important to investigate community-specific obstacles and assess the long-term sustainability of current efforts to promote vaccination among marginalized migrant groups. Further research into global immunization disparities among immigrant populations is crucial.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".