Understanding information about COVID-19: how sources’ reliability and level of understanding influenced adherence to sanitary measures in Canada
Bibliographic record
Abstract
Abstract Previous studies have highlighted the importance of promoting health literacy and minimizing misinformation to encourage higher adherence to key sanitary measures during the COVID-19 pandemic. This study explores how one’s understanding of information and sources’ reliability can hinder adherence to sanitary measures implemented by the Canadian government. Data was collected from a representative sample of 3,617 Canadians, following a longitudinal design of 11 measurement times from April 2020 to April 2021. Overall, a low level of understanding was associated with membership in lower adherence trajectories to sanitary measures. Adjusted odds ratio (AOR) showed it was between 3 and 34 times more likely for participants with low understanding to be in the lowest adherence trajectory. Information sources’ reliability also showed a significant effect on adherence trajectories for social distancing and staying home (AOR: between 1.5 and 2.5). These results are discussed considering future policy implications.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".