Return to school and mask-wearing in class during the COVID-19 pandemic: Student perspectives from a school simulation study
Bibliographic record
Abstract
Abstract Objectives This study examined children’s perspectives about returning to in-person school following lockdown due to the pandemic and about mask-wearing in class, as well as the mental health of children and parents during the pandemic. Methods This cross-sectional study was part of a 2-day school simulation exercise that randomized students to different masking recommendations. Parent-report of mental health and post-simulation child-report of COVID-19-related anxiety and mask-wearing were analyzed using descriptive and multiple regression analyses. Semi-structured focus groups were conducted with older students to supplement questionnaire data. Results Of 190 students in this study, 31% were in grade 4 or lower 95% looked forward to returning to in-person school. Greater child anxiety about COVID-19 was predicted by increased parent/caregiver anxiety (β=0.67; P<0.001), and lower parental educational attainment (β=1.86; P<0.002). Older students were more likely than younger students to report that mask-wearing interfered with their abilities to interact with peers (χ2(1)=31.16; P<0.001) and understand the teacher (χ2(1)=13.97; P<0.001). Students in the group that did not require masks were more likely than students in the masking group to report worries about contracting COVID-19 at school (χ2(1)=10.07; P<0.05), and anticipated difficulty wearing a mask (χ2(1)=18.95; P<0.001). Conclusions For children anxious about COVID-19, parental anxiety and education about COVID-19 may be targets for intervention. Future research should examine the impact of prolonged implementation of public health mitigation strategies in school on academic achievement and children’s mental health.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".