Public health guideline compliance and perceived government effectiveness during the COVID-19 pandemic in Canada: Findings from a longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has imposed enormous adversity worldwide. Public health guidelines have been a first line of defense but rely on compliance with evolving recommendations and restrictions. This study sought to characterize adherence to and perceptions of public health guidelines over a one-year timeframe during the pandemic. METHODS: Participants were 1435 community adults in Ontario who completed assessments at five time points (April 2020, July 2020, October 2020, January 2021, and April 2021; 92% retention). Participants were assessed for self-reported adherence to government protocols and perceptions of government response (importance, compliance, and effectiveness). Analyses used general linear mixed-effects modelling of overall changes by time and examined differences based on age and sex. FINDINGS: Over time, participants reported high or increasing behavioural engagement in public health guidelines, including physical distancing, restricting activity, and masking. In contrast, participants exhibited significant reductions in perceived importance and compliance, with evidence of more negative changes in younger participants. The largest changes were a substantial reduction in perceived government effectiveness, from predominantly positive perceptions to predominantly negative perceptions. INTERPRETATION: These results illuminate evolving trends in public health compliance and perceptions over the course of the pandemic in Canada, revealing the malleability of public perceptions of public health recommendations and government effectiveness. FUNDING: This research was funded by a grant from the Canadian Institutes of Health Research (CIHR). CIHR had no role in study design, data collection, data analysis, interpretation, or writing of the report.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".