Knowledge and preventive behaviors regarding COVID-19 in Bangladesh: A nationwide distribution
Bibliographic record
Abstract
Assessing individuals' knowledge and preventive behaviors towards the Coronavirus Disease of 2019 (COVID-19) is essential for the related public health surveillance strategies. Although some of the studies were conducted in Bangladesh, none of these studies considered the geographical distribution of knowledge and preventive behaviors towards COVID-19. Therefore, the present nationwide cross-sectional study with 10,067 samples for the first-time aims to assess the knowledge gap by presenting the geographical distribution of the COVID-19 knowledge and preventive behaviors across all administrative districts of Bangladesh. The measures included socio-demographics and questions about knowledge and preventive behaviors related to COVID-19. One-way ANOVA, independent t-test, and multiple linear regression were used to analyze the data. In addition, GIS-based mapping identified district-wise distribution of the outcomes. Results indicated that the overall mean score of knowledge related to COVID-19 was 14.363 ± 3.073, whereas 16.95 ± 2.89 was for preventive behaviors. Participants' being male, being divorced or widowed, consuming alcohol, smoking cigarettes, living in villages, and having no formal education reported lower performing preventive COVID-19 behaviors. Those participants with higher knowledge scores reported higher preventive COVID-19 behaviors (β = 0.053, p<0.001). However, the model predicted only 13.2% of the variation in preventive COVID-19 behaviors while the overall model being significant. The findings suggest that the Bangladeshi government should initiate appropriate far-reaching program of health education focusing on knowledge and preventive behaviors towards COVID-19 at a community level. After all, the strategies to combat COVID-19 will require individuals' involvement to control and prevent the disease outbreak, for which education is essential.
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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 | 0.000 | 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) | 0.002 | 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".