A Survey of Allergic Consumers and Allergists on Precautionary Allergen Labelling: Where Do We Go from Here?
Bibliographic record
Abstract
BACKGROUND: Despite the widespread use of precautionary allergen labelling (PAL) by manufacturers, PAL is not always used consistently and can be a source of misinterpretation by consumers and allergists. Although its use is not specifically regulated in Canada, some voluntary guidelines exist. The aims of this study were to investigate allergic consumers' and clinicians' understanding of PAL, to describe consumers' attitudes towards products with PAL, and to examine recommendations given by clinicians to their patients about these products. We also compared two groups of consumers enrolled in this study, since the majority of them (72%) were registered in the Food Allergy Canada database and the others (28%) came from representative consumers of the general population. METHODS: An online survey was sent from 2 to 28 December 2021 to allergic consumers registered with Food Allergy Canada's database and to a group of allergic consumers extracted from a panel representative of the general population and not registered with Food Allergy Canada (third-party panel). All consumer participants had a food allergy or were a parent/caregiver of a child with food allergy and had to be diagnosed by a medical professional. Considering that consumers registered via the Food Allergy Canada database could be more informed about labelling than the third-party consumer panel, we conducted a multivariate analysis (logistic regression) on the key variables related to PAL allowing to compare these two groups of participants. In addition, a separate online survey was sent to allergist members of the Canadian Society of Allergy and Clinical Immunology and provincial associations to investigate their understanding of PAL from 12 November 2021 to 16 January 2022. RESULTS: A total of 1080 consumers and 63 allergists (29% of allergists in Canada) responded to the surveys. Fifty percent of consumers were adults with food allergy, and 50% were a parent/caregiver of a child with food allergy. Food allergy was diagnosed most commonly by an allergist in 76% of the cases. Fifty-four percent of consumers purchased products with a PAL statement at least occasionally, and more than half of consumers (53%) considered PAL a very useful tool. Most surveyed individuals (59%) had not heard of the term "individual allergen threshold" or had heard the term but did not know what it meant. The same allergic consumers were reluctant to buy food products with even a small amount of their allergen (i.e., a dose that would not trigger an allergic reaction in the vast majority of them). Half of allergists reported PAL was not useful in its current form, and 83% supported the consumption of foods with PAL to their patients in some circumstances. CONCLUSION: While most consumers are somewhat confident in the accuracy of ingredient information on pre-packaged foods, interpretation of PAL remains confusing by many allergic consumers. If changes are to be made based on allergen thresholds, a multi-stakeholder approach will be required with greater consumer and allergist education on risk assessment concepts to facilitate the implementation of allergen population thresholds into the application of PAL.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".