COVID-19 Health Precautions: Identifying Demographic and Socio-Economic Disparities and Changes over Time
Bibliographic record
Abstract
The recent coronavirus disease 2019 (COVID-19) pandemic has required the adoption of precautionary health behaviours to reduce the risk of infection. This study examines adherence, as well as changes in adherence, to four key precautionary behaviours among Canadian adults: wearing face masks, social distancing, hand washing, and avoiding large crowds. Data are drawn from Series 3 and 4 of the nationally representative Canadian Perspectives Survey Series, administered by Statistics Canada in June and July 2020. We calculate overall adherence levels as well as changes over time. Logistic regression models estimate each behaviour as a function of demographic and socio-economic characteristics to identify adherence disparities across population segments. We find a nearly universal increase in precautionary behaviours from June to July in mask wearing (67.3 percent to 83.6 percent), social distancing (82.4 percent to 89.2 percent), and avoiding crowds (84.1 percent to 88.9 percent); no significant change occurred in the frequency of hand washing. We observe significant disparities in adherence to precautionary behaviours, especially for mask wearing, in June; female, older, immigrant, urban, and highly educated adults were significantly more likely to adhere to precautionary behaviours than male, younger, Canadian-born, rural, and low-educated adults. By July 2020, these disparities persisted or were slightly attenuated; women, however, had consistently higher adherence to all behaviours at both time points. These findings have substantial implications for policy and potential public health interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".