Elementary School Teachers’ Perceptions of COVID-19-Related Restrictions on Food Allergy Management
Bibliographic record
Abstract
(1) Background: Approximately 7% of Canadian children live with a food allergy (FA). Pre-COVID-19, ~20% of anaphylactic reactions occurred in schools. Yet, teachers reported poor FA-related knowledge, and experiences during the COVID-19 pandemic are not well-studied. Additionally, teachers' management approaches vary widely. We aimed to describe elementary school teachers' perceptions about FA management during the COVID-19 pandemic; (2) Methods: Using a semi-structured interview guide, English-speaking elementary school teachers in Winnipeg, Canada were interviewed virtually. Interviews were audio-recorded and transcribed verbatim. Data were analysed thematically; (3) Results: Most teachers were female and taught in public schools. Two themes were identified. Theme 1, COVID-19 restrictions made mealtimes more manageable, capturing the positive impacts of pandemic restrictions such as seating arrangements and enhanced cleaning. Limited lunchtime supervision prompted some teachers to assume this role. Theme 2, Food allergy management was indirectly adapted to fit changing COVID-19 restrictions, describing how changing restrictions influenced FA-related practices. FA training was offered virtually with less nursing support. Class cohorts and remote learning decreased teachers' perceived risk and FA-related management responsibility; (4) Conclusions: COVID-19-related practices were perceived as positively influencing in-school FA management, although unintended consequences, such as increased supervisory roles for teachers and reduced nursing support, were described.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".