Human Behaviour in Response to Canadian COVID-19 Public Health Measures in a Pre-Vaccine Era
Bibliographic record
Abstract
This dissertation aims to explore Canadians’ attitude and behaviour responses to the COVID-19 pandemic and its associated public health measures. First, a cross-sectional survey was used to describe attitudes and behaviours towards the Canadian COVID-19 public health response and identify risk-modifying behaviours, based on sociodemographic characteristics. Second, adherence to physical distancing recommendations, and its impact on transmission, were assessed using contact patterns derived from four cross-sectional contact diary surveys. Third, changes over time in precautionary behaviours, and support for public health measures in Canada, were evaluated using a longitudinal survey design. Finally, a disease transmission model explored the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario, Canada. Several key findings resulted from this research. The cross-sectional analysis demonstrated a high degree of perceived effectiveness and perceived ability to comply with public health measures in May 2020. However, members of the paid workforce, those with the lowest income levels, and younger age groups without paid sick leave were 50-60% less likely to be confident that they would be able to isolate for the required period if they had symptoms of COVID-19. Contact pattern data collected between May and December 2020 demonstrated that most Canadians were adhering to COVID-19 public health measures. The large number of reported contacts in workplace and school settings in September and December 2020 emphasizes the need to support and ensure infection control procedures in both workplaces and schools. The longitudinal analysis demonstrated that respondents’ behaviour mirrored government guidance between July and November 2020 and respondents supported individual precautionary behaviour, and limitations on non-essential businesses over school closures. The disease transmission model demonstrated that voluntary avoidance behaviour in the absence of government action was not sufficient to mitigate transmission of disease during the second wave of COVID-19 in Ontario. The combined approach of cross-sectional and longitudinal surveys as well as disease transmission modelling has provided insight that can be used to target messaging, develop policies, and provide supports to encourage uptake of the necessary public health 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".