COVID-19 Knowledge, Attitudes, and Practices Among People in Bangladesh: Telephone-Based Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: The world has been grappling with the COVID-19 pandemic, a dire public health crisis, since December 2019. Preventive and control measures have been adopted to reduce the spread of COVID-19. To date, the public's knowledge, attitudes, and practices regarding COVID-19 across Bangladesh have been poorly understood. Therefore, it is important to assess people's knowledge, attitudes, and practices (KAP) toward the disease and suggest appropriate strategies to combat COVID-19 effectively. OBJECTIVE: This study aimed to assess the KAP of Bangladeshi people toward COVID-19 and to identify their determinants. METHODS: We conducted a country-wide cross-sectional telephonic survey from May 7 to 29, 2020. A purposive sampling method was applied, and adult Bangladeshi citizens who have mobile phones were approached to participate in the survey. Interviews were conducted based on verbal consent. Multiple logistic regression analyses and several tests were performed to identify the factors associated with KAP related to COVID-19. RESULTS: A total of 492 of 576 Bangladeshi adults aged 18 years and above completed the interview, with a response rate of 85.4% (492/576). Of the 492 participants, 321 (65.2%) were male, and 304 (61.8%) lived in a rural area. Mean scores for knowledge, attitudes, and practices were 10.56 (SD 2.86), 1.24 (SD 0.83), and 3.17 (SD 1.5), respectively. Among the 492 respondents, 273 (55.5%) had poor knowledge, and 251 (49%) expressed a negative attitude; 192 out of 359 respondents (53.5%) had poor practices toward COVID-19. Mean scores of knowledge, attitudes, and practices differed significantly across various demographic and socioeconomic groups. Rural residents had lower mean scores of knowledge (mean 9.8, SD 3.1, P<.001) and adherence to appropriate practice measures (mean 4, SD 1.4, P<.001) compared to their urban counterparts. Positive and statistically strong correlations between knowledge and attitudes (r=0.21, P<.001), knowledge and practices (r=0.45, P<.001), and attitudes and practices (r=0.27, P<.001) were observed. Television (53.7%) was identified as the major source of knowledge regarding COVID-19. Almost three-quarters of the respondents (359/492, 73%) went outside the home during the lockdown period. Furthermore, the study found that good knowledge (odds ratio [OR] 3.13, 95% CI 2.03-4.83, and adjusted OR 2.33, 95% CI 1.16-4.68) and a positive attitude (OR 2.43, 95% CI 1.59-3.72, and adjusted OR 3.87, 95% CI 1.95-7.68) are significantly associated with better practice of COVID-19 health measures. CONCLUSIONS: Evidence-informed and context-specific risk communication and community engagement, and a social and behavior change communication strategy against COVID-19 should be developed in Bangladesh based on the findings of this study, targeting different socioeconomic groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".