Knowledge mobilization between the food industry and public health nutrition scientists: findings from a case study
Bibliographic record
Abstract
Abstract 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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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| 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".