Bibliographic record
Abstract
Conventional food systems lead to environmental degradation and nutrient deficiencies, contributing to poor health. Conceptualizing dietetic practice by practice areas limits how dietitians (RDs) see the relevance of food systems to practice and potential actions to contribute to more healthy, sustainable food systems. RDs are well-positioned to promote sustainable food systems (SFS) because they work across the food system landscape: Food Production, Economic, Political, Consumer Demand, and Health Systems. In this article, we invite RDs to view SFS activities through a systems lens, bringing more awareness to their impact. This includes identifying leverage points - actions that have a larger impact than their immediate outcome - and unintended negative consequences of recommendations. We discuss findings from a scoping review that included 11 peer-reviewed and 16 grey literature articles and chart practice activities, tools and recommendations according to practice area and the food system landscape. Findings demonstrate there are several existing activities and areas for further growth. Framing practice activities across the food system landscape, facilitates understanding of the interconnected nature of the food system to get to the root cause of problems. As a self-determining profession, dietitians determine the degree of influence they have in supporting the transition to sustainable dietary patterns.
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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 | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".