Access, acceptability, and uptake of the COVID-19 vaccine among global migrants: A rapid review
Bibliographic record
Abstract
OBJECTIVE: To conduct a rapid review and determine the acceptability, access, and uptake of the COVID-19 vaccine among global migrants. MATERIALS AND METHODS: A rapid review was conducted May 2022 capturing data collected from April 2020 to May 2022. Eight databases were searched: PubMed, Ovid Medline, EMBase, CINAHL, SCOPUS, Google Scholar, LILACS, and the Web of Science. The keywords "migrants" AND COVID-19" AND "vaccine" were matched with terms in MeSH. Peer-reviewed articles in English, French, Portuguese, or French were included if they focused on COVID-19 immunization acceptability, access, or uptake among global migrants. Two independent reviewers selected and extracted data. Extracted data was synthesized in a table of key characteristics and summarized using descriptive statistics. RESULTS: The search returned 1,186 articles. Ten articles met inclusion criteria. All authors reported data on the acceptability of the COVID-19 vaccine, two on access, and one on uptake. Eight articles used quantitative designs and two studies were qualitative. Overall, global migrants had low acceptability and uptake, and faced challenges in accessing the COVID-19 vaccine, including technological issues. CONCLUSIONS: This rapid review provides a global overview of the access, acceptability, and uptake of the COVID-19 vaccine among global migrants. Recommendations for practice, policy, and future research to increase access, acceptability, and uptake of vaccinations are discussed.
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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 | 0.021 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".