“We don’t have such a thing, that you may be allergic”: Newcomers’ understandings of food allergies in Canada
Bibliographic record
Abstract
Food allergies are emerging as important public health risks in Canada, affecting 3-4% of adults and 6-7% of children. Despite much lower prevalence rates among recent immigrants (i.e. in the country less than 10 years), evidence has shown this population to be more concerned about the risks of food allergies than the general population and have unique experiences around purchasing foods for allergen-free environments. As a substantial and growing segment of the Canadian population, it is important to understand newcomers' perceptions and knowledge of food allergies and related policies developed to protect allergic children (e.g. nut-free schools and or classrooms). This paper draws upon the results of focus groups conducted with newcomers from food allergic households (i.e. directly affected), as well as those with school-aged children who have to prepare or buy foods for allergen-controlled classrooms or schools (i.e. indirectly affected) living in Mississauga, Ontario. Results indicate unique challenges and understandings of food allergies as a new and unfamiliar risk for most newcomers, particularly as the indirectly affected participants negotiate the policy landscape. The directly affected group highlights the supportive environment in Canada resulting from the same policies and increased awareness in the general population.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".