Media Source Characteristics Regarding Food Fraud Misinformation According to the Health Information National Trends Survey (HINTS) in China: Comparative Study
Notice bibliographique
Résumé
BACKGROUND: Ongoing rumors and fake news regarding food fraud, adulteration, and contamination are highly visible. Health risk information circulating through media and interpersonal communication channels has made health crisis an important research agenda. OBJECTIVE: This study explored the issue of food fraud and the effect of misinformation. Further, it assessed whether and how these issues have provided evidence-based interventions for food handlers and regulators to mitigate fraud misinformation. METHODS: The Health Information National Trends Survey (HINTS) was adopted for a collaborative study in China, after which a cross-sectional survey with door-to-door interviews was performed. Participants from Beijing and Hefei were selected using multistage sampling of adults in May 2017. Based on 4 government surveillance reports on food rumors and safety incidents, a descriptive analysis, correlation analysis, and analysis of variance were performed on the data. RESULTS: A total of 3090 results were gathered and analyzed. Among the respondents, 83.6% (2584/3090) heard at least one food rumor. Learning about food fraud was correlated with interpersonal connections (eg, doctors or health specialists) for accessing food health information. Overall, Chinese citizens with a higher level of interpersonal connection were more likely to be concerned about food incidents with a statistical difference (P<.001). Interpersonal connection was the most frequent communication source (698/1253, 55.7%), followed by traditional media (325/1253, 25.9%) and internet portals (144/1253, 11.5%). There was a significant relationship between media use and media category in Beijing (P<.001) and Hefei (P<.001). Overall, responses to food fraud and incident risks were lower in Beijing than in Hefei (P=.006). The respondents in Beijing were confronted more frequently by food rumors (range 346-1253) than those in Hefei (range 155-946). The urban dwellers in Beijing and their rural counterparts in Hefei also differed in terms of perceiving different levels of food risks from different media sources. The food rumor narratives that examined the conspiracy belief showed that social media played more important roles in influencing attitudes against misinformation for users in Hefei than in Beijing. CONCLUSIONS: This study shows that consumers have to be on guard against not only fake food, but also spreading fake information and rumors, as well as conspiracy beliefs involving fake food. This study focused on characterizing media sources, types of food fraud misinformation, and risk perceptions of food safety, which mix urgency and suspicion, and attempted to provide evidence-based interventions for risk management guidance, with the hypothesis of significant correlations between media types and sources, and consumer exposure and perception levels of food rumors and risks.
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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 | 0,017 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,005 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».