A national cross-sectional survey of public perceptions, knowledge, and behaviors during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Introduction Efforts to mitigate the global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have largely relied on broad compliance with public health recommendations yet navigating the high volume of evolving information and misinformation related to SARS-CoV-2 can be challenging. We assessed national public perceptions (e.g., severity, concerns, health), knowledge (e.g., transmission, information sources), and behaviors (e.g., physical distancing) related to COVID-19 in Canada to understand public perspectives and inform future public health initiatives. Methods We administered a national online survey with the goal of obtaining responses from 2000 adults residing in Canada. Respondent sampling was stratified by age, sex, and region. We used descriptive statistics to summarize respondent characteristics and tested for significant overall regional differences using chi-squared tests and t-tests, as appropriate. Results We collected 1,996 eligible questionnaires between April 26 th and May 1 st , 2020. One-fifth (20%) of respondents knew someone diagnosed with COVID-19, but few had tested positive themselves (0.6%). Negative impacts of pandemic conditions were evidenced in several areas, including concerns about healthcare (e.g. sufficient equipment, 52%), pandemic stress (45%), and worsening social (49%) and mental/emotional (39%) health. Most respondents (88%) felt they had good to excellent knowledge of virus transmission, and predominantly accessed (74%) and trusted (60%) Canadian news television, newspapers/magazines, or non-government news websites for COVID-19 information. We found high compliance with distancing measures (80% either self-isolating or always physical distancing). We identified regional differences in perceptions, knowledge, and behaviors related to COVID-19. Discussion We found that knowledge about COVID-19 is largely acquired through domestic news sources, which may explain high self-reported compliance with prevention measures. The results highlight the broader impact of a pandemic on the general public’s overall health and wellbeing, outside of personal infection. The study findings should be used to inform public health communications during COVID-19 and future pandemics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".