Understanding the factors associated with COVID-19 vaccine hesitancy in Venezuela
Bibliographic record
Abstract
Abstract Background Despite nearly a quarter of Venezuelans remaining unvaccinated against coronavirus disease 2019 (COVID-19), the factors contributing to vaccine hesitancy in the country have not been thoroughly investigated. Methods A cross-sectional study was conducted from October 15th to 30th, 2022, using a knowledge, attitudes, and practices (KAP) survey to identify factors associated with COVID-19 vaccine hesitancy. Results The study analyzed data from 1,930 participants from all 24 states of Venezuela. The majority (93.4%) were vaccinated. The mean age was 40 years, predominantly female (67.3%), and held a university degree (70.6%). The mean KAP score was significantly higher among vaccinated individuals compared to unvaccinated ones (7.79 vs. 3.94 points for knowledge, 40 vs. 24 points for attitudes, and 16 vs. 10 points for practices, all p < 0.001). Increases in the scores for KAP were associated with increased odds of being vaccinated (84.6%, 25.6%, and 33% respectively for each one-point increase, all p < 0.001). Certain demographic factors such as marital status, occupation, religious beliefs, monthly income, and location influence COVID-19 vaccine knowledge. Higher income and certain occupations decrease the odds of low knowledge, while residing in specific states increases it. Attitudes towards the COVID-19 vaccine are influenced by age, health status, vaccination status, and location. Higher income and absence of certain health conditions decrease the odds of negative attitudes. Lastly, age, occupation, monthly income, and location affect vaccine practices. Advanced age and higher income decrease the odds of inappropriate practices, while residing in La Guaira state increases them. Conclusion Factors such as age, education level, occupation, monthly income, and location were found to be associated with knowledge and attitudes towards COVID-19 vaccine among the surveyed Venezuelans.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".