The General Public Knowledge, Attitude, and Practices Regarding COVID-19 During the Lockdown in Asian Developing Countries
Bibliographic record
Abstract
The recent outbreak of coronavirus disease (COVID-19) is the worst global crisis. Since no successful treatment and vaccine have been reported, efforts to improve the public's knowledge, attitudes, and practices are critical to reducing the spread of COVID-19. This study aims to investigate the general public knowledge, attitude, and practices regarding COVID-19. A cross-sectional online survey was conducted in three developing countries (China, India, and Pakistan). The reason for choosing only three countries is to identify the cross-border effect statistically and data collection constraints. The IBM SPSS version 23.0 was used for descriptive, univariate, and multivariate analysis of the study. One thousand one hundred and sixty participants completed the study, one-quarter of them were female, and three-quarters were male. The study's findings evidenced that the knowledge and attitude correlation was 58.4% and between knowledge and practices 18.2%. Furthermore, the knowledge was found lower in females, among India and Pakistan, and people aged less and equivalent to 30 years. The attitudes among respondents were found poorer among unmarried females and India and Pakistan residents. While the practices found lower among employed, unemployed and, respondents had a bachelor's degree, and females reside in India. And future studies should focus on factors that influence the government regarding the imposition of lockdown, boost the economy in the pandemic, and motivate the general public to follow the health institution's instructions.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".