Do Canadians have favourable attitudes towards reintroducing mask mandates?
Bibliographic record
Abstract
Like many countries, Canada faced a triple threat of respiratory illness (COVID-19, influenza, and respiratory syncytial virus) in the fall of 2022, after all COVID-19 measures had been lifted, placing significant pressure on the healthcare system (Tanne, 2022;Wright, 2022).Public health agencies were therefore urging the population to maintain preventive behaviours like masking indoors to help slow the spread of these viruses (Wright, 2022).Mask-wearing has been associated with a lower incidence of COVID-19 and has been shown to prevent the spread of respiratory viruses in general, including COVID-19 (Aravindakshan et al., 2022;Howard et al., 2021;Talic et al., 2021).Although governments recommended mask-wearing indoors, no mandates were reintroduced (Canadian Institute for Health Information, (n.d.); Wright, 2022).This may have been influenced by the perception that the population would reject the mandate, or worse, that it might trigger mass protests (Gaviola, 2022;Montpetit & MacFarlane, 2020).To optimize our COVID-19 responses and future pandemic preparedness, it would be important to determine the population's attitudes towards the reintroduction of mask mandates under conditions of heightened risk, and their socio-demographic predictors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".