Knowledge mobilization between food industry and public health nutrition scientists: learnings from a case study
Bibliographic record
Abstract
Abstract Background: Improving the nutritional quality of the food supply make the access to nutritious foods easier, which enhance food habits and population health. Yet, knowledge mobilization initiatives between public health nutrition researchers and food industries are often not well considered and understood. This study explored key elements to consider in order that researchers can better mobilize nutritional scientific knowledge with food industries to encourage nutritive improvement of food products. Method: A qualitative approach of a case study was selected to answer the research question, using semi-structured interviews as the data collection technique. Quebec baking industry actors were shown a mock-up of an online mobilization platform sharing the results of the Food Quality Observatory describing the nutritional quality of breads offered in Quebec, Canada. They were asked to think aloud while exploring the web platform and being interviewed. Two coders analyzed the data using an inductive approach, starting with individual open coding, and then joining their analyses and forming thematic categories. Results: The final data consisted of 10 semi-structured interviews conducted between October 2019 and August 2020 (average duration of 95 minutes). Codes were agglomerated into four main themes: the industries background, the knowledge mobilization initiative, the product-related matters stemming from the information shared and the industries’ feeling of motivation. Within each theme, sub-themes were highlighted and related to the industries’ motivation to improve their products’ nutritional quality level. Specific to the case studied, this research also specified key considerations for sodium and fiber changes 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 with 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 to better understand the themes highlighted in this study. Classification codes: Public Health, Public Private, Policy Making, Research Institutions, Use of Knowledge
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".