Knowledge, Attitude, and hesitance toward COVID-19 vaccination -a cross-sectional study from West Bengal
Bibliographic record
Abstract
This cross-sectional study investigates knowledge, attitudes, and hesitancy toward COVID-19 vaccination among 300 adults in West Bengal, India. Using stratified random sampling, participants were selected to represent diverse age, gender, and rural-urban demographics, resulting in 58.3% rural and 41.7% urban respondents. Data were collected through a pre-tested, structured questionnaire (Cronbach’s alpha = 0.79) assessing socio-demographics, vaccine knowledge, attitudes, hesitancy, vaccination status, and information sources. Findings reveal that 65.0% of respondents perceived the vaccine as safe, but only 50.3% trusted its development, with 26.0%–28.7% uncertainty across knowledge items. Hesitancy was high, with 56.0% scoring 16–25 on a 5–25 scale, and 41.3% remaining unvaccinated. Social media and TV/newspapers were the primary information sources (28.3% each), while health workers were cited by only 20.7%. Linear regression (R² = 0.727) identified younger age (β = -2.3093, p < .001), lower education (β = -2.2400, p < .001), and rural location (β = -3.3785, p < .001) as significant predictors of higher hesitancy, while gender and income were non-significant. These results highlight substantial knowledge gaps and hesitancy, particularly in rural and less-educated groups, underscoring the need for targeted health education and improved access to credible information to enhance vaccination uptake in West Bengal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".