Remember the past, plan for the future: How interactions between risk perception and behavior during the COVID-19 pandemic can inform future Canadian public health policy
Bibliographic record
Abstract
The ongoing COVID-19 pandemic necessitated the implementation of numerous temporary public health policies, including social distancing, masking, and movement limitations. These types of measures require most citizens to follow them to be effective at a population level. This study examined population adherence to emergency public health measures using early data collected in the Spring of 2020, when all Canadian jurisdictions were under relatively strict measures. In total, 1,369 participants completed an online questionnaire package to assess adherence, perceptions of government response, and perceptions of COVID-19 risk. Results indicated that most Canadians were pleased with the government's handling of the early phases of the pandemic and immediately engaged new public health mandates. Willingness to change behaviors was unrelated to satisfaction with the government response. Similarly, behavioral adherence was also unrelated to satisfaction with government, or personal risk perceptions; however, adherence to public health guidelines was related to elevated psychological distress. As the pandemic continues, public health officials must balance the mental health of the population with the physical health concerns posed by COVID-19 when applying public health mandates.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".