Interactions Between Nutrition Professionals and Industry: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: In recent years, interactions between nutrition professionals (NPs) and the food industry, such as sponsorship arrangements, have raised concerns, particularly as these may negatively impact the trustworthiness and credibility of the nutrition profession. This study aimed to map the literature and identify knowledge gaps regarding interactions between NPs and industry. We sought to examine the nature of such interactions and NPs perspectives about these, as well as the risks and solutions. METHODS: We conducted a scoping review according to a pre-registered protocol, searching eight electronic databases and grey literature sources in March 2021 to identify documents for inclusion. Two independent reviewers screened citations for inclusion and conducted data extraction. Quantitative and qualitative syntheses were conducted. RESULTS: We identified 115 documents for analysis, published between 1980 to 2021, with a majority from the United States (n=59, 51%). Only 32% (n=37) were empirical studies. The food industry was the most frequent industry type discussed (n=91, 79%). We identified 32 types of interactions between NPs and industry, such as continuing education provided by industry and sponsorship of professional bodies and health and nutrition organizations. The financial survival of nutrition organizations and continuing education access for NPs were the most frequently cited advantages of industry-NPs interactions. On the other hand, undermining public trust, NPs credibility and public health nutrition recommendations were pointed out as risks of these interactions. Following a code of ethics, policies, or guidelines was the most frequently proposed solution for managing these interactions. CONCLUSION: Interactions between nutrition professionals and industry actors: A scoping review protocol. doi:10.17605/OSF.IO/Q6PUA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".