Contextual and Psychological Factors of Face Mask-Wearing Among Secondary School Students: A Cross-Sectional Survey from Toronto
Bibliographic record
Abstract
Background: Several studies have investigated the factors associated with mask-wearing in adults, but very few studies have explored mask wearing in children or schools. As such, our study aims to explore the knowledge, attitudes, and psychological factors impacting mask-wearing in high school students. Methods: In February 2023, a cross-sectional survey was distributed online to Grade 9–12 students in a school located in Toronto. The association between knowledge, attitudes and psychological variables was evaluated using descriptive statistics and Kendall’s tau-b rank correlation tests. Thematic analysis was then used to summarize the qualitative responses. Results: A total of 62 participants fully completed the survey. 46.8% (n = 29) identified as male, 43.5% (n = 27) as female, and the median age was 16 years (IQR, 15–17 years). Almost all participants indicated that they were knowledgeable about COVID-19's modes of transmission (n = 57, 92.0%) and preventative measures (n = 60, 96.8%). More participants supported voluntary masking in schools (n = 34, 54.9%) compared to mask mandates (n = 16, 25.9%). Attitudes towards masking in schools, perceived susceptibility, perceived aesthetic, and anxiety-reduction benefits were positively correlated with higher frequencies of mask-wearing (τb = 0.238 to 0.491, p = 0.03 to <0.001). However, perceived barriers (e.g., physical discomfort, hindrance to communication, inconvenience) were negatively correlated (τb = -0.259 to -0.385, p = 0.0019 to <0.001). Age, sex, self-reported COVID-19 knowledge, perceived severity, and perceived medical benefits were not significant correlates of masking behaviour. Qualitative reasons that encouraged individuals to wear masks included protection and aesthetic reasons. Students who did not wear masks questioned the necessity of mask-wearing and commented on the new social norm of not wearing a mask among their classmates. Conclusion: Adolescent mask-wearing is significantly correlated with pre-existing attitudes towards masks, while perceived barriers strongly discourage students from wearing masks. Future research should investigate how to best promote positive beliefs regarding mask-wearing to youth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".