Knowledge, attitudes and perceptions towards COVID-19 vaccinations: a cross-sectional community survey in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Several vaccines have been approved for use against coronavirus disease (COVID-19) and distributed globally in different regions. However, general community knowledge, attitudes and perceptions towards COVID-19 vaccinations are poorly understood. Thus, the study aimed to investigate community knowledge, attitudes and perceptions towards COVID-19 vaccinations in Bangladesh. METHODS: An exploratory and anonymous population-based e-survey was conducted among 1658 general individuals (55.6% male; mean age = 23.17 ± 6.05 years; age range = 18-65 years). The survey was conducted using a semi-structured and self-reported questionnaire containing informed consent along with four sections (i.e., socio-demographics, knowledge, attitudes, and perceptions). Multiple linear regression was performed to determine the variables predicting knowledge, and attitudes towards COVID-19 vaccinations. RESULTS: The mean scores of knowledge and attitudes were 2.83 ± 1.48 (out of 5) and 9.34 ± 2.39 (out of 12) respectively. About a quarter of participants thought that the COVID-19 vaccination available in Bangladesh is safe, only 60% will have the vaccination and about two-thirds will recommend it to family and friends. In the multiple regression model, higher SES, having university/ higher levels of education, having nuclear families and having previous history of essential vaccines uptake were associated with knowledge; whilst attitudes were significantly associated with being female and having previous history of essential vaccines uptake. Just over half of the participants thought that everyone should be vaccinated and 61% responded that health workers should be vaccinated first on priority basis. 95% of respondents believed the vaccine should be administered free of charge in Bangladesh and almost 90% believed that the COVID-19 vaccine used in Bangladesh may have side effects. CONCLUSIONS: The findings reflect inadequate knowledge but more positive attitudes towards COVID-19 vaccine among the general population in Bangladesh. In order to improve knowledge, immediate health education programs need to be initiated before mass vaccination are scheduled.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".