Evaluating the Public Relationships of Registered Dietitians with Government, Food Industry, and Universities Regarding Knowledge Transfer
Bibliographic record
Abstract
Purpose: The nutritional quality of the food supply is influenced by actions from the government, the food industry, and universities. Considering the potential of registered dietitians (RDs) to transfer nutrition knowledge, this study aimed to assess the positivity of the relationships between RDs and these actors to improve knowledge transfer initiatives for the benefit of food nutritional quality. Methods: Using a descriptive and correlation research design, 68 RDs, members of the Ordre des diététistes-nutritionnistes du Québec (dietitians in the Province of Québec, Canada), evaluated their relationships with the government, the food industry, and universities in a web-based survey. Results: Overall, RDs rated their relationships with universities more positively than with government or industry, which were similarly rated (p < 0.001). More specifically, RDs working in clinical nutrition rated all dimensions of their relationships with the actors in general less favourably than RDs in other sectors (p < 0.05). Conclusions: To support the successful transfer of knowledge regarding the nutritional quality of foods, these findings suggest that RDs should be encouraged to attempt to develop more positive relationships with government and industry, while maintaining their positive relationships with universities. Future research could further examine the clinical RD subgroup as well as the reasons for these results by conducting in-depth interviews or group discussions.
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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 | 0.033 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".