Demographic characteristics associated with food allergy in a Nationwide Canadian Study
Bibliographic record
Abstract
INTRODUCTION: We conducted a nationwide Canadian telephone survey on food allergy prevalence between February 2016 and January 2017, targeting vulnerable populations (New, Indigenous, and lower-income Canadians). OBJECTIVE: To examine the independent effect of demographic characteristics on food allergy. METHODS: Canadian households with vulnerable populations were targeted using Canadian Census data and the household respondent reported whether each household member had a perceived (self-reported) or probable (self-report of a convincing history or physician diagnosis) food allergy. The association between perceived and probable food allergy and demographic characteristics was assessed through weighted multivariable random effects logistic regressions. RESULTS: Children, females, Canadian-born participants, adults with post-secondary education, and those residing in smaller households were more likely to report perceived or probable food allergy. Although immigrant parents self-reported less food allergy, Canadian-born children of Southeast/East Asian immigrant versus other immigrant or Canadian-born parents reported more food allergy. CONCLUSION: We have demonstrated clear associations between demographic characteristics and food allergy, which may provide important clues to the environmental determinants of food allergy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".