Gluten-Free Product Recalls and Their Impact on Consumer Trust
Bibliographic record
Abstract
The range of gluten-free food products available to consumers is steadily expanding. In recent years, recalls of food products have highlighted the importance of accurate labeling of food products for the presence of wheat, other gluten-containing cereals, or gluten itself as refined ingredient. The purpose of this study was to gain more insights into recent food recalls related to undeclared gluten/wheat contamination and consumer experiences with these recalls. Recalls of products triggered by gluten contamination are relatively scarce and are not often triggered by a consumer complaint. The impact of these recalls on consumer trust was evaluated through an online survey that was distributed among supporters of Celiac Canada (CCA) and covered (i) strategies to adhere to a gluten-free diet, (ii) experiences with gluten-free recalls and their impact on consumer trust, and (iii) demographic information. Consumer concern regarding gluten-free product recalls is significant, but the concern regarding recalls is not heightened after experiencing a recall. Companies pursuing transparency in the process, identification of the source of contamination, and mitigation strategies going forward are likely to retain consumer trust in their product and brand. Based on the survey results, further efforts focusing on consumer education regarding interpreting nutrient labels, identifying sources of information on product recalls, and understanding procedures to follow upon suspected gluten contamination of a gluten-free product are recommended.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".