Understanding the international provision of allergen information picture in the non-prepacked sector
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,165 | 0,276 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,002 | 0,008 |
| Communication savante | 0,017 | 0,020 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».