Characteristics Associated with the Dual Behavior of Mask Wearing and Vaccine Acceptance: A Pooled Cross-Sectional Study among Adults in Saskatchewan
Bibliographic record
Abstract
While the dual behavior of consistent mask wearing and vaccine acceptance represents an effective method of protecting oneself and others from COVID-19, research has yet to directly examine its predictors. A total of 3347 responses from a pooled cross-sectional survey of adults living in Saskatchewan, Canada, were analyzed using a multinomial logistic regression model. The outcome variable was the combined behavior of mask-wearing and vaccine intention in four combinations, while covariates consisted of socio-demographic factors, risk of exposure to coronavirus, mitigating behaviors, and perceptions of COVID-19. Those who were 65 years and older, financially secure, consistently practiced social distancing and had no or very few contacts with people outside their households, were concerned about spreading the virus, and perceived they would be seriously sick if infected were likely to engage in both mask wearing and vaccine acceptance, rather than one or the other, with adjusted odds ratios ranging from 2.24 to 27.54. Further, within mask wearers, these factors were associated in a graded manner with vaccine intent. By describing the characteristics of those who engage in both mask wearing and vaccine acceptance, these results offer a specific set of characteristics for public health authorities to target and, therefore, contribute to the rapidly evolving body of knowledge on protective factors for COVID-19.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".