Investigating self‐reported food allergy prevalence in Waterloo Region, Canada
Bibliographic record
Abstract
While food allergy prevalence has been studied at the national level, we know little of food allergy prevalence or perceptions of prevalence/management at the local level. This paper uses Waterloo Region as a case study to 1) document self‐reported individual and household food allergy and sensitivity prevalence at the local level; 2) investigate perceptions of food allergy prevalence; and 3) explore perceived confidence in anaphylaxis management. Survey data were collected from January to March 2019. Respondents (n = 500) self‐reported individual and household food allergy and sensitivity, estimated the percentage of Canadians with food allergy, and were queried about their knowledge of food allergy management. Prevalence estimates were weighted to the structure of the 2016 Canadian Census, and univariate and bivariate analysis were conducted. Prevalence of self‐reported food allergy was 12.1% (95%CI, 8.8%‐15.3%), and prevalence of self‐reported food sensitivity was 26.3% (95%CI, 21.9%‐30.7%). When asked to estimate the percentage of Canadians with food allergy, the mean perceived percentage was 35.1% (SD = 22.96). Self‐reported prevalence of food allergy appears higher in Waterloo Region, and the estimated percentage of Canadians with food allergy is inflated. Understanding prevalence and perceptions at the local level is important for targeted allocation of public health resources to ensure safe spaces for individuals with food allergy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".