Impact of COVID-19 Public Health Protocols on Teachers Instructing Children and Adolescents During an In-Person Simulation.
Bibliographic record
Abstract
Objective: As a result of the COVID-19 pandemic, public health agencies and school boards across Canada enacted new protocols, including face masks, physical distancing and enhanced hygiene, to support the safe reopening of in-person school. This study explored the experiences and perceptions of teachers instructing children and adolescents in person during a two-day school simulation. Method: This study was part of a large school simulation exercise conducted in Toronto, Ontario. Kindergarten to grade 12 teachers taught in classrooms with either masked students, or students who were un-masked or only masked when physical distancing was not possible. A qualitative descriptive phenomenology approach was utilized, and data were collected via virtual focus groups. Qualitative data analysis involved multiple rounds of inductive coding to generate themes. Results: . The majority of teachers reported that mask-wearing and physical distancing impacted their classroom teaching, communication and connection with students. Conclusions: As schools transition to in-person instruction, teachers will be required to play dual roles in education and public health, with implications on safety, teaching and professional identity. Public health agencies and school boards are encouraged to engage teachers in ongoing conversations regarding in-person school planning and operations. Furthermore, evidence-based interventions, including increased teaching development programs, are recommended to support teachers during the COVID-19 pandemic.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".