Knowledge About COVID-19 in Brazil: Cross-Sectional Web-Based Study
Bibliographic record
Abstract
BACKGROUND: COVID-19 is a highly transmissible illness caused by SARS-CoV-2. The disease has affected more than 200 countries, and the measures that have been implemented to combat its spread, as there is still no vaccine or definitive medication, have been based on supportive interventions and drug repositioning. Brazil, the largest country in South America, has had more than 140,000 recorded deaths and is one of the most affected countries. Despite the extensive quantity of scientifically recognized information, there are still conflicting discussions on how best to face the disease and the virus, especially with regard to social distancing, preventive methods, and the use of medications. OBJECTIVE: The main purpose of this study is to evaluate the Brazilian population's basic knowledge about COVID-19 to demonstrate how Brazilians are managing to identify scientifically proven information. METHODS: A cross-sectional study design was used. An original online questionnaire survey was administered from June 16 to August 21, 2020, across all five different geopolitical regions of the country (ie, the North, Northeast, Center-West, Southeast, and South). The questionnaire was comprised of questions about basic aspects of COVID-19, such as the related symptoms, conduct that should be followed when suspected of infection, risk groups, prevention, transmission, and social distancing. The wrong questionnaire response alternatives were taken from the fake news combat website of the Brazilian Ministry of Health. Participants (aged ≥18 years) were recruited through social networking platforms, including Facebook, WhatsApp, and Twitter. The mean distributions, frequencies, and similarities or dissimilarities between the responses for the different variables of the study were evaluated. The significance level for all statistical tests was less than .05. RESULTS: A total of 4180 valid responses representative of all the states and regions of Brazil were recorded. Most respondents had good knowledge about COVID-19, getting an average of 86.59% of the total score with regard to the basic aspects of the disease. The region, education level, age, sex, and social condition had a significant association (P<.001) with knowledge about the disease, which meant that women, the young, those with higher education levels, nonrecipients of social assistance, and more economically and socially developed regions had more correct answers. CONCLUSIONS: Overall, Brazilians with social media access have a good level of basic knowledge about COVID-19 but with differences depending on the analyzed subgroup. Due to the limitation of the platform used in carrying out the study, care should be taken when generalizing the study findings to populations with less education or who are not used to accessing social networking platforms.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".