A Sociodemographic Profile of Mask Use During the COVID-19 Outbreak Among Young and Elderly Individuals in Brazil: Online Survey Study
Bibliographic record
Abstract
BACKGROUND: Sociodemographic variables may impact decision making regarding safety measures. The use and selection of adequate face masks is a safety and health measure that could help minimize the spread of COVID-19 infection. OBJECTIVE: This study aims to examine sociodemographic variables and factors relating to COVID-19 that could impact decision making or the choice to use or not use face masks in the prevention and care of a possible COVID-19 infection among a large sample of younger and older Brazilian people. METHODS: An online survey composed of 14 closed-ended questions about sociodemographic variables and COVID-19 was used. A total of 2673 participants consisted of Brazilian people (aged ≥18 years) from different states of Brazil and were grouped according to age (≤59 years and ≥60 years). To compare the variables of interest (associated with wearing a face mask or not), chi-square and likelihood ratio tests were used (with P<.05 being significant). RESULTS: Most of the participants in both groups were women from the southeast region who had postgraduate degrees. Approximately 61% (1452/2378) of individuals aged ≤59 years and 67.8% (200/295) of those aged ≥60 years were not health professionals. In the group aged ≤59 years, 83.4% (1983/2378) did not show COVID-19 signs and symptoms, and 97.3% (2314/2378) were not diagnosed with COVID-19. In the older adult group, 92.5% (273/295) did not show signs and symptoms of COVID-19, and 98.3% (290/295) were not diagnosed with the disease. The majority of the participants in both groups reported using face masks, and their decision to use face masks was influenced by the level of education and their occupation as a health professional. CONCLUSIONS: Younger and older adults have worn face masks during the COVID-19 outbreak. It is difficult to measure how much of a positive impact this attitude, habit, and behavior could have on the degree of infection and spread of the disease. However, it can be a positive indicator of adherence to the population's security and safety measures during the pandemic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".