A systematic review and meta-regression of the knowledge, practices, and training of restaurant and food service personnel toward food allergies and Celiac disease
Notice bibliographique
Résumé
BACKGROUND: Up to 3-5% of adults may be affected by food allergies, while approximately 1% are affected by Celiac disease (CD). Food allergy reactions can be severe and potentially fatal, while CD can result in various symptoms. Restaurant and food service establishment staff have an important role in helping to prevent food allergy and CD risks among affected customers. OBJECTIVES: A systematic review was undertaken to identify, characterize, and synthesize published research on the prevalence of food allergy and CD knowledge, practices, and training among restaurant and food service personnel. The population of interest included any personnel in these settings who prepare, handle, or serve food. Outcomes included the prevalence of food allergy and CD knowledge, practices, and training. METHODS: The review was conducted using standardized methods, including: a comprehensive search strategy; relevance screening of abstracts; characterization of relevant articles; data extraction; and risk of bias assessment. Outcomes were stratified into comparable subgroups and descriptively analyzed to examine prevalence trends across studies. Meta-regression was conducted on selected outcomes to identify possible sources of variability in prevalence estimates across studies. RESULTS: Thirty-eight relevant studies were identified; most were conducted in the United States (50%) and focused on food allergies (90%). Significant variability was identified across studies for most outcomes. Participants generally had a higher knowledge, self-efficacy, and use of practices related to preparing and serving allergen-free meals compared to food allergy emergency response. Participants' reported use of various risk prevention and response practices was generally low. Most participants across studies had not received prior food allergy training (median prevalence of 65% across 12 studies). IMPLICATIONS: Key knowledge and practice gaps were identified that could be targeted by future training programs. Research gaps were also identified, including a need for more experimental studies to evaluate food allergy and CD training interventions.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».