Interactions between registered dietitians and the food industry in Canada: results from a cross-sectional survey
Bibliographic record
Abstract
In recent years, relationships between nutrition professionals and the food industry have raised concerns over the risks they may pose to the profession's credibility and integrity. However, empirical research on the nature and frequency of these interactions, as well as how professionals manage them, is limited. An online cross-sectional survey was conducted among 167 Registered Dietitians (RDs) from Quebec, Canada, regarding (i) the nature and frequency of their interactions with the industry, (ii) their perceptions of existing risks and benefits of those interactions, (iii) the strategies they employed to manage these interactions, and (iv) their confidence levels in managing those interactions. RDs in Quebec have experienced, on average, 1.7 interactions per month with the industry over the past year (May 2022-May 2023). The three most frequently reported interactions were (i) receiving targeted communications from the industry, (ii) participating in continuing education provided by the industry, and (iii) receiving educational materials for professionals and consumers created by the industry. Overall, RDs acknowledged benefits associated with interacting with the industry (e.g. improving the food quality of products on the market) but also identified risks (e.g. compromising professional independence) and discussed strategies to mitigate these risks, including referring to their Code of Ethics (56.9%). In conclusion, RDs in Quebec engage with the food industry in various ways. Perceptions of risks and benefits related to these interactions vary considerably, highlighting the need for training and standardized strategies to minimize conflicts of interest and manage these interactions effectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".