Understanding the international provision of allergen information picture in the non-prepacked sector
Bibliographic record
Abstract
Our rapid evidence assessment aimed to develop an understanding of the international provision of allergen information in the non-prepacked food sector. A mixed-methods approach was used, including a rapid literature and data review, stakeholder interviews, as well as co-production panel review with our advisor (Dr Audrey DunnGalvin) and members of Allergy UK and the FSA. We found legislation on nine of the 18 countries within the scope for this project. These included three EU countries who have also brought in additional national requirements to EU legislation (Lithuania, Republic of Ireland, and Netherlands); two non-EU countries that align to EU legislation and have additional legislation in place (Switzerland, and Norway); three non-European countries (US, Philippines, and Canada) have legislation in place or draft form; and the UK. While legislation was not found in English for the other countries, all 27 EU member states follow the EU legislation as a minimum requirement. The UK follows EU legislation as we were a member state at the time of implementation. The UK has since left the EU; however the legislation has been retained. The UK has additional legislation for food that is prepacked for direct sale (PPDS), but not other types of non-prepacked food. There is considerable variation across countries and regions, in terms of type of allergens and foods covered, the required format of provision of allergen information (e.g., verbal or written) and the food establishments included within the legislation. Across all countries included within the review, the use of precautionary allergen labelling was voluntary. The overall objective of this rapid evidence assessment was to develop recommendations for the FSA to inform future policy and regulation decisions based on evidence of ‘what works’. However, the reviewed literature provided no evidence of whether approaches are associated with improved safety, compliance, unintended consequences, or feasibility. We were also unable to infer effectiveness via data on reported trends in deaths or incidents pre and post implementation of legislation, as these data was not found for any country. Similarly, there was not enough evidence to allow a systematic analysis of incidents associated with different types or categories of food business operators (FBOs) selling non-prepacked foods. We are therefore unable to provide clear recommendations of ‘what works’ from the evidence. We have instead gathered information on the ideas or potential solutions suggested in the literature.
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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 | 0.165 | 0.276 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".