Perception and Practice of Bangladeshi Adults Towards the Prevention of COVID-19: A Statistical Analysis
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) has continued to spread across the world with increasing numbers of confirmed cases and deaths. Due to outbreaks of new variants of the virus and limited treatment options, positive perception and good practice of preventive guidelines have remained essential measures for the prevention of the disease and slowing down its transmission. We aimed to study perception towards COVID-19 and the practice of guidelines for preventing the disease among Bangladeshi adults during the early stage of the rapid rise of the outbreak. Methods: Data was collected data from 320 participants. For measuring their level of practice, we asked a general question: “Are you properly following the WHO-recommended guidelines to avoid COVID-19?” The frequency distribution, Chi-square (χ2) test and binary logistic regression model were used in this study. Results: The average risk perception among the participants was 3.05±0.75 (median, 3.00) (95% CI of mean: 2.96-3.13) where the score ranges from 0 (no risk) to 4 (high risk). More than 27% of participants showed high-risk perceptions. Males (p<0.05), high educated (p<0.05), rich (p<0.01), service holders (p<0.05), and younger adults (p<0.05) had higher odds of high-risk perception. More than 71% of participants had a good practice of always following the WHO guidelines to prevent COVID-19 and living locations in urban areas (p<0.01), high education (p<0.01), rich (p<0.01), and joint family (p<0.01) had the most contributions to good practice. Conclusions: The study findings revealed that special attention should be given to rural areas, and individuals of low literacy, education and socioeconomic level to more effectively prevent COVID-19.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".