Understanding COVID-19 Vaccine Acceptance among Latin American Health Workers: Implications for Designing Interventions
Bibliographic record
Abstract
Health workers (HWs) have a key role in promoting vaccine acceptance. This study draws on the Behavioral and Social Drivers of Vaccination (BeSD) model and our team's investigation of vaccine hesitancy in a sample of 1197 HWs across 14 Caribbean countries in 2021. We conducted a cross-sectional Internet survey of 6718 HWs across 16 countries in Latin America in spring 2022, after the COVID-19 vaccine had recently become widely available in the region. The survey assessed HWs' attitudes regarding COVID-19 vaccines and vaccines in general. As a proxy measure of COVID-19 vaccine acceptance, we used the willingness to recommend the COVID-19 vaccine to eligible people. Ninety-seven percent of respondents were COVID-19 vaccine acceptant. Although nearly all respondents felt that the COVID-19 vaccine was safe and effective, 59% expressed concerns about potential adverse effects. Despite uniformly high acceptance of the COVID-19 vaccine overall and across Latin American subregions, acceptance differed by sex, HW profession, and COVID-19 history. Social processes, including actions and opinions of friends, family, and colleagues; actions and opinions of religious leaders; and information seen on social networks shaped many respondents' opinions of vaccines, and the magnitude of these effects differed across both demographic and geographic subgroups. Information campaigns designed for HWs should underscore the importance of vaccine safety. Messages should be tailored to specific audiences according to the information source each is most likely to consult and trust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".