Sociodemographic disparities in knowledge, practices, and ability to comply with COVID-19 public health measures in Canada
Bibliographic record
Abstract
Abstract The effectiveness of public health interventions for mitigation of the coronavirus (COVID-19) pandemic depends on individual attitudes and the level of compliance toward these measures. We surveyed a representative sample of the Canadian population about risk perceptions, attitudes, and behaviours towards the Canadian COVID-19 public health response. Our analysis demonstrates that these risk perceptions, attitudes, and behaviours varied by several demographic variables identifying a number of areas in which policies could help address issues of public adherence. Examples include targeted messaging for men and younger age groups, social supports for those who need to self-isolate but may not have the means to do so, changes in workplace policies to discourage presenteeism, and provincially co-ordinated masking and safe school reopening policies. Taken together such measures are likely to mitigate the impact of the next pandemic wave in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".