Knowledge mobilization between the food industry and public health nutrition scientists: findings from a case study
Notice bibliographique
Résumé
BACKGROUND: Improving the nutritional quality of the food supply increases access to nutritious foods, which improves dietary habits and population health. Yet, knowledge mobilization initiatives between public health nutrition researchers and food industries are often not adequately considered and understood. This study explored what elements related to this specific context need to be recognized so that researchers can better mobilize nutrition science knowledge with the food industry to promote the nutritional improvement of food products. METHOD: A case study qualitative approach was selected to answer the research question, using semi-structured interviews as the data collection technique. Québec baking industry actors were shown a mock-up of an online mobilization platform sharing the results of the Food Quality Observatory that describes the nutritional quality of breads offered in Québec, Canada. They were asked to think aloud as they explored the web platform and were interviewed. Two coders analyzed the data using an inductive approach and thematic content analysis, starting with individual open coding, and then put forward their analyses and drafted the final themes. RESULTS: The final data consisted of 10 semi-structured interviews conducted between October 2019 and August 2020. Four main themes were identified: the industry's context, the knowledge mobilization initiative, the product-related matters stemming from the information shared and the motivation within the industry. Within each theme, sub-themes were highlighted and related to the industries' motivation to improve their products' nutritional quality. This study also specified key considerations for changes to the sodium and fiber content in bread. CONCLUSION: Other steps beyond using simple language and a website format could be taken to better mobilize scientific knowledge with food industries, such as providing more consumer information, using an integrated knowledge mobilization approach that includes a consideration of ethics, working with communication professionals, collaborating with food science experts, and providing resources to act on shared information. Legislation such as the front-of-pack regulations could accelerate the pace of collaboration between researchers and industry. Overall, establishing a prior relationship with industries could help gain a better understanding of the themes highlighted in this study. Future research could build on this case study to provide more insights and solidify these findings. CLASSIFICATION CODES: Public Health, Public Private, Policy Making, Research Institutions, Use of Knowledge.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| 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 ».