Assessing effective mask use by the public in two countries: an observational study
Bibliographic record
Abstract
OBJECTIVES: During the COVID-19 pandemic wearing a mask in public has been recommended in some settings and mandated in others. How often this advice is followed, how well, and whether it inadvertently leads to more disease transmission opportunities due to a combination of improper use and physical distancing lapses is unknown. DESIGN: Cross-sectional observational study performed in June-August 2020. SETTING: Eleven outdoor and indoor public settings (some with mandated mask use, some without) each in Toronto, Ontario, and in Portland, Oregon. PARTICIPANTS: All passers-by in the study settings. OUTCOME MEASURES: Mask use, incorrect mask use, and number of breaches (ie, coming within 2 m of someone else where both parties were not properly masked). RESULTS: We observed 36 808 persons, the majority of whom were estimated to be aged 31-65 years (49%). Two-thirds (66.7%) were wearing a mask and 13.6% of mask-wearers wore them incorrectly. Mandatory mask-use settings were overwhelmingly associated with mask use (adjusted OR 79.2; 95% CI 47.4 to 135.1). Younger age, male sex, Torontonians, and public transit or airport settings (vs in a store) were associated with lower adjusted odds of wearing a mask. Mandatory mask-use settings were associated with lower adjusted odds of mask error (OR 0.30; 95% CI 0.14 to 0.73), along with female sex and Portland subjects. Subjects aged 81+ years (vs 31-65 years) and those on public transit and at the airport (vs stores) had higher odds of mask errors. Mask-wearers had a large reduction in adjusted mean number of breaches (rate ratio (RR) 0.19; 95% CI 0.17 to 0.20). The 81+ age group had the largest association with breaches (RR 7.77; 95% CI 5.32 to 11.34). CONCLUSIONS: Mandatory mask use was associated with a large increase in mask-wearing. Despite 14% of them wearing their masks incorrectly, mask users had a large reduction in the mean number of breaches (disease transmission opportunities). The elderly and transit users may warrant public health interventions aimed at improving mask use.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".