Understanding and addressing COVID-19 vaccine hesitancy in low and middle income countries and in people with severe mental illness: Overview and recommendations for Latin America and the Caribbean
Bibliographic record
Abstract
Despite the speedy development of vaccines for COVID-19, their rollout has posed a major public health challenge, as vaccine hesitancy (VH) and refusal are high. Addressing vaccine hesitancy is a multifactorial and context-dependent challenge. This perspective focuses on VH in the world region of Latin America and the Caribbean (LAC) and includes people suffering from severe mental illness, therefore covering populations and subpopulations often neglected in scientific literature. We present an overview of VH in LAC countries, discussing its global and historical context. Vaccine uptake has shown to widely vary across different subregions of LAC. Current data points to a possible correlation between societal polarization and vaccination, especially in countries going through political crises such as Brazil, Colombia, and Venezuela. Poor accessibility remains an additional important factor decreasing vaccination rollout in LAC countries and even further, in the whole Global South. Regarding patients with severe mental illness in LAC, and worldwide, it is paramount to include them in priority groups for immunization and monitor their vaccination coverage through public health indicators.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".