Public perceptions during the first wave of the COVID-19 pandemic in Canada: a demographic analysis of self-reported beliefs, behaviors, and information acquisition
Bibliographic record
Abstract
INTRODUCTION: We explored associations between sociodemographic factors and public beliefs, behaviors, and information acquisition related to the coronavirus disease 2019 (COVID-19) to identify how the experiences of subpopulations in Canada may vary. METHODS: We administered a national online survey through Ipsos Incorporated to adults residing in Canada. Sampling was stratified by population age, sex, and regional distributions. We used descriptive statistics to summarize responses and test for differences based on gender, age, educational attainment, and household income using chi-squared tests, followed by weighted logistic regression. RESULTS: We collected 1996 eligible questionnaires between April 26th and May 1st, 2020. Respondents mean age was 50 years, 51% were women, 56% had a post-secondary degree, and 72% had a household income <$100,000. Our analysis found differences within the four demographic groups, with age effects most acutely evidenced. Respondents 65 years and older were more likely to perceive the pandemic as very serious, less likely to report declines in overall health, and more likely to intend to get vaccinated, compared to 18-29 year olds. Women overall were more likely to report negative outcomes than men, including stress due to the pandemic, and worsening social, mental/emotional, and spiritual health. Respondents 45 and older were more likely to seek and trust information from traditional Canadian news sources, while 18-29 year olds were more likely to seek and trust information on social media; overall, women and respondents with a post-secondary degree were more likely to access and trust online information from public health sites. CONCLUSION: This study found important demographic differences in how adults living in Canada perceived the COVID-19 pandemic, the impacts on their health, and their preferences for information acquisition. Our results highlight the need to consider demographic characteristics in tailoring the format and information medium to improve large scale acceptance and uptake of mitigation and containment measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".