Winnipeg-based elementary school teachers’ perspectives on food allergy management: a qualitative analysis
Bibliographic record
Abstract
Abstract Background Food allergy affects 7-8% of children worldwide. Teachers supervise children in school, where most children spend their day. Yet, teachers have variable food allergy-related knowledge. Objective We aimed to identify how Winnipeg-based elementary school teachers manage food allergy and prevent food-triggered allergic reactions in their classrooms and schools. Methods Kindergarten-Grade 6 public and private school teachers, from Winnipeg, Canada, were interviewed virtually upon providing written informed consent. Interviews were recorded and transcribed verbatim. The study followed a pragmatic framework. Data were analysed via thematic analysis by multiple researchers. Results We interviewed 16 teachers, who primarily identified as female (87.5%). Most teachers worked in public schools (87.5%) and, on average, had 5.8 years of teaching experience. We identified four themes within the data. Most teachers (68.9%) had direct or indirect experience with food allergy. Theme 1 described the minimal standardization and inconsistent enforcement of food allergy policies between and within schools. Teachers also had varied food allergy knowledge. Theme 2 reflected teachers’ variable confidence/perceived knowledge towards food allergy management, including feeling of stress and anxiety. Theme 3 captured the lack of standardized food allergy education for teachers, and concerns about the adequacy of the current provincial program. Theme 4 described how teachers spoke of relying on other school staff, families and students to have effective communication. Conclusion Teachers’ food allergy management was informed by their knowledge and lived experience, guided by their school policies and individualized students’ needs. Teachers identified gaps in knowledge and communication, and desired more training and resources.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".