Reported handwashing practices of Vietnamese people during the COVID-19 pandemic and associated factors: a 2020 online survey
Bibliographic record
Abstract
COVID-19 pandemic currently affects nearly all countries and regions in the world. Washing hands, together with other preventive measures, to be considered one of the most important measures to prevent the disease. This study aimed to characterize reported handwashing practices of Vietnamese people during the COVID-19 pandemic and associated factors. Kobo Toolbox platform was used to design the online survey. There were 837 people participating in this survey. All independent variables were described by calculating frequencies and percentages. Univariate linear regression was used with a significant level of 0.05. Multiple linear regression was conducted to provide a theoretical model with collected predictors. Seventy-nine percent of the respondents used soap as the primary choice when washing their hands. Sixty percent of the participants washed their hands at all essential times, however, only 26.3% practiced washing their hands correctly, and only 28.4% washed their hands for at least 20 seconds. Although 92.1% washed hands after contacting with surfaces at public places (e.g., lifts, knob doors), only 66.3% practiced handwashing after removing masks. Females had better reported handwashing practices than male participants (OR = 1.88; 95% CI: 1.15-3.09). Better knowledge of handwashing contributed to improving reported handwashing practice (OR = 1.30; 95% CI: 1.20-1.41). Poorer handwashing practices were likely due, at least in part, to the COVID-19 pandemic information on the internet, social media, newspapers, and television. Although the number of people reported practicing their handwashing was rather high, only a quarter of them had corrected reported handwashing practices. Communication strategy on handwashing should emphasize on the minimum time required for handwashing as well as the six handwashing steps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".