What can we learn to increase vaccination in Latin America: Factors associated with COVID-19 vaccination
Bibliographic record
Abstract
Background: Even though vaccination may serve to effectively overcome the COVID-19 pandemic, vaccine hesitancy is still prevalent and affected by different variables. This research is intended to understand which variables influence the likelihood of an individual getting a COVID-19 vaccine in a sample from Latin America, applying the COM-B model. Method: 368 individuals from Latin America answered a self-administered, cross-sectional survey from the iCARE study. Survey data began in March 2020 using convenience snowball sampling (globally) and parallel representative sampling in targeted countries. Results: A structural equation model showed that knowing that getting vaccinated will help protect others, wanting to contribute to high vaccination rates among the population to achieve herd immunity, and believing that getting vaccinated would reduce personal worries and anxiety predict the likelihood of an individual getting vaccinated. This shows that in this sample, motivators are more salient than capabilities and opportunities regarding vaccination uptake. Conclusions: Campaigns to reduce vaccine hesitancy need to highlight the prosocial factors of getting vaccinated and increase vulnerability and risk perceptions regarding the disease.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".