Vaccines for all? A rapid scoping review of COVID-19 vaccine access for Venezuelan migrants in Latin America
Bibliographic record
Abstract
INTRODUCTION: The entangled health and economic crises fueled by COVID-19 have exacerbated the challenges facing Venezuelan migrants. There are more than 5.6 million Venezuelan migrants globally and almost 80% reside throughout Latin America. Given the growing number of Venezuelan migrants and COVID-19 vulnerability, this rapid scoping review examined how Venezuelan migrants are considered in Latin American COVID-19 vaccination strategies. MATERIAL AND METHODS: We conducted a three-phased rapid scoping review of documents published until June 18, 2021: Peer-reviewed literature search yielded 142 results and 13 articles included in analysis; Gray literature screen resulted in 68 publications for full-text review and 37 were included; and official Ministry of Health policies in Argentina, Brazil, Chile, Colombia, Ecuador, and Peru were reviewed. Guided by Latin American Social Medicine (LASM) approach, our analysis situates national COVID-19 vaccination policies within broader understandings of health and disease as affected by social and political conditions. RESULTS: Results revealed a heterogeneous and shifting policy landscape amid the COVID-19 pandemic which strongly juxtaposed calls to action evidenced in literature. Factors limiting COVID-19 vaccine access included: tensions around terminologies; ambiguous national and regional vaccine policies; and pervasive stigmatization of migrants. CONCLUSIONS: Findings presented underscore the extreme complexity and associated variability of providing access to COVID-19 vaccines for Venezuelan migrants across Latin America. By querying the timely question of how migrants and specifically Venezuelan migrants access vaccinations findings contribute to efforts to both more equitably respond to COVID-19 and prepare for future pandemics in the context of displaced populations. These are intersectional and evolving crises and attention must also be drawn to the magnitude of Venezuelan mass migration and the devastating impact of COVID-19 in the region. Integration of Venezuelan migrants into Latin American vaccination strategies is not only a matter of social justice, but also a pragmatic public health strategy necessary to stop COVID-19.
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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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".