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Record W4414157077 · doi:10.3390/nu17182916

Food Allergy and Foodservice: A Comparative Study of Allergic and Non-Allergic Consumers’ Behaviors, Attitudes, and Risk Perceptions

2025· article· en· W4414157077 on OpenAlexafffundabout
Fatemeh Shirani, Silvia Fraga Domínguez, Jérémie Théolier, Jennifer Gerdts, Kate Reid, Sébastien La Vieille, Samuel Benrejeb Godefroy

Bibliographic record

VenueNutrients · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsHealth CanadaAllerGen
FundersFood Allergy CanadaUniversité Laval
KeywordsFood allergySeriousnessAllergyRisk perceptionPerceptionPopulationSample (material)Food safety

Abstract

fetched live from OpenAlex

Background: Food-allergic reactions in restaurants often result from miscommunication between customers with allergies and staff, or from staff members’ insufficient knowledge of food allergies. This study examined the behaviors, attitudes, and risk perceptions of food-allergic consumers when dining out or ordering from foodservice establishments (FSEs) compared to consumers without food allergies. Methods: A representative pan-Canadian survey was conducted amongst three groups: one of individuals without food allergies (n = 500) and two of food-allergic individuals (allergic-convenience sample [n = 500] and allergic-general population [n = 500]). The convenience sample comprised members of Food Allergy Canada, a national patient advocacy organization. Some participants with food allergies had experienced reactions linked to an FSE (43% convenience, 27% general). Weighted responses from food-allergic groups were compared to those of non-allergic ones using chi-square (p < 0.05). Statistical comparison between allergic groups was not attempted due to inherent differences in their allergic condition. Results: In several questions, responses from the non-allergic group differed significantly from those of the allergic-convenience sample, but not from those of the allergic-general population. Food-allergic-convenience respondents were more likely to avoid ordering food or dining out than non-allergic ones, with the highest avoidance (66%) noted for third-party platforms. Cost was the main barrier for non-allergic and allergic-general populations, whereas the allergic-convenience sample prioritized allergy-related concerns. Although at a lower rate than for participants with food allergies, food allergies influenced restaurant selection for 44% of participants without food allergies when dining with individuals outside their household. Most allergic respondents perceived that FSEs underestimate the seriousness of food allergies (82% convenience, 71% general), yet they felt safe while dining out (60% convenience, 85% general), pointing at loyalty to specific FSEs as a risk mitigation strategy. Conclusions: This study highlights a potentially higher burden of disease (psychological and social strain, reduced quality of life) among a subgroup of the food-allergic population (convenience sample), as reflected in their behaviors, attitudes, and risk perceptions towards meals prepared in FSEs. Nevertheless, both allergic groups expressed shared concerns and needs related to safety (e.g., ingredient disclosure for all menu items, prevention of allergen cross-contact, ability of an FSE to offer a safe meal, establishing clear communication processes for allergy-related information), which FSEs and regulators should consider when designing risk management strategies.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.028
GPT teacher head0.290
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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